Click publication for abstract and additional information. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
PDF | DOI: 10.1016/j.gaitpost.2023.08.010
Background: Real-world mobility involves walking in challenging conditions. Assessing gait during simultaneous physical and cognitive challenges provides insights on cognitive health. Research question: How does uneven surface, cognitive task, and their combination affect gait quality and does this gait performance relate to cognitive functioning? Methods: Community-dwelling older adults (N = 104, age=75 ± 6 years, 60 % females) performed dual-task walking paradigms (even and uneven surface; with and without alphabeting cognitive task (ABC)) to mimic real-world demands. Gait quality measures [speed(m/s), rhythmicity(steps/minute), stride time variability (%), adaptability (m/s2), similarity, smoothness, power (Hz) and regularity] were calculated from an accelerometer worn on the lower back. Linear-mixed modelling and Tukey analysis were used to analyze independent effects of surface and cognitive task and their interaction on gait quality. Partial Spearman correlations compared gait quality with global cognition and executive function. Results: No interaction effects between surface and cognitive task were found. Uneven surface reduced gait speed(m/s) (β = –0.07). Adjusted for speed, uneven surface reduced gait smoothness (β = –0.27) and increased regularity (β = 0.09), Tukey p less than .05, for even vs uneven and even-ABC vs uneven-ABC. Cognitive task reduced gait speed(m/s) (β = –0.12). Adjusted for speed, cognitive task increased variability (β = 7.60), reduced rhythmicity (β = –6.68) and increased regularity (β = 0.05), Tukey p less than .05, for even vs even-ABC and uneven vs uneven-ABC. With demographics as covariates, gait speed was not associated with cognition. Gait quality [lower variability during even-ABC (ρp =–.31) and uneven-ABC (ρp =–.28); greater rhythmicity (ρp between.22 and.29) and greater signal-adaptability AP (ρp between.22 and.26) during all walking tasks] was associated with better global cognition. Gait adaptability during even (ρp =–0.21, p = 0.03) and uneven(ρp =–0.19, p = 0.04) walking was associated with executive function. Significance: Surface and cognitive walking tasks independently affected gait quality. Our study with high-functioning older adults suggests that task-related changes in gait quality are related to subtle changes in cognitive functioning.
PDF | DOI: 10.1016/j.bandc.2023.106063
Improving postural control in older adults is necessary for reducing fall risk, and prefrontal cortex activation may also play a role. We sought to examine the impact of exercise interventions on postural control and prefrontal cortex activation during standing balance tasks. We hypothesized that balance would improve and prefrontal control would be reduced. We assessed a subset of participants enrolled in a randomized trial of two exercise interventions. Both groups completed strength and endurance training and the experimental treatment arm included training on timing and coordination of stepping. Postural control and prefrontal cortex activation were measured during dual-task standing balance tasks before and after the intervention. Eighteen participants in the standard strengthening and mobility training arm and 16 in the timing and coordination training arm were included. We examined pre- to post-intervention changes within each study arm, and compared them between interventions. Results did not show any pre- to post-intervention changes on standing postural control nor prefrontal cortex activation in either arm. In addition, there were no differences between the two intervention arms in either balance or prefrontal activation. While exercise interventions can improve mobility, we do not demonstrate evidence of improved standing balance or prefrontal control in standing.
PDF | DOI: 10.1007/s40520-023-02503-x
Accelerometers provide an opportunity to expand standing balance assessments outside of the laboratory. The purpose of this narrative review is to show that accelerometers are accurate, objective, and accessible tools for balance assessment. Accelerometry has been validated against current gold standard technology, such as optical motion capture systems and force plates. Many studies have been conducted to show how accelerometers can be useful for clinical examinations. Recent studies have begun to apply classification algorithms to accelerometry balance measures to discriminate populations at risk for falls. In addition to healthy older adults, accelerometry can monitor balance in patient populations such as Parkinson's disease, multiple sclerosis, and traumatic brain injury. The lack of software packages or easy-to-use applications have hindered the shift into the clinical space. Lack of consensus on outcome metrics has also slowed the clinical adoption of accelerometer-based balance assessments. Future studies should focus on metrics that are most helpful to evaluate balance in specific populations and protocols that are clinically efficacious.
PDF | DOI: 10.1109/TNNLS.2022.3145365
Artificial intelligence and machine learning techniques have progressed dramatically and become powerful tools required to solve complicated tasks, such as computer vision, speech recognition, and natural language processing. Since these techniques have provided promising and evident results in these fields, they emerged as valuable methods for applications in human physiology and healthcare. General physiological recordings are time-related expressions of bodily processes associated with health or morbidity. Sequence classification, anomaly detection, decision making, and future status prediction drive the learning algorithms to focus on the temporal pattern and model the nonstationary dynamics of the human body. These practical requirements give birth to the use of recurrent neural networks (RNNs), which offer a tractable solution in dealing with physiological time series and provide a way to understand complex time variations and dependencies. The primary objective of this article is to provide an overview of current applications of RNNs in the area of human physiology for automated prediction and diagnosis within different fields. Finally, we highlight some pathways of future RNN developments for human physiology.
PDF | DOI: 10.1038/s41591-023-02396-3
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.
PDF | DOI: 10.1002/lary.30222
Background: Upper esophageal sphincter opening (UESO), and laryngeal vestibule closure (LVC) are two essential kinematic events whose timings are crucial for adequate bolus clearance and airway protection during swallowing. Their temporal characteristics can be quantified through time-consuming analysis of videofluoroscopic swallow studies (VFSS). Objectives: We sought to establish a model to predict the odds of penetration or aspiration during swallowing based on 15 temporal factors of UES and laryngeal vestibule kinematics. Methods: Manual temporal measurements and ratings of penetration and aspiration were conducted on a videofluoroscopic dataset of 408 swallows from 99 patients. A generalized estimating equation model was deployed to analyze association between individual factors and the risk of penetration or aspiration. Results: The results indicated that the latencies of laryngeal vestibular events and the time lapse between UESO onset and LVC were highly related to penetration or aspiration. The predictive model incorporating patient demographics and bolus presentation showed that delayed LVC by 0.1 s or delayed LVO by 1% of the swallow duration (average 0.018 s) was associated with a 17.19% and 2.68% increase in odds of airway invasion, respectively. Conclusion: This predictive model provides insight into kinematic factors that underscore the interaction between the intricate timing of laryngeal kinematics and airway protection. Recent investigation in automatic noninvasive or videofluoroscopic detection of laryngeal kinematics would provide clinicians access to objective measurements not commonly quantified in VFSS. Consequently, the temporal and sequential understanding of these kinematics may interpret such measurements to an estimation of the risk of aspiration or penetration which would give rise to rapid computer-assisted dysphagia diagnosis.
PDF | DOI: 10.1109/JBHI.2022.3224323
Dysphagia occurs secondary to a variety of underlying etiologies and can contribute to increased risk of adverse events such as aspiration pneumonia and premature mortality. Dysphagia is primarily diagnosed and characterized by instrumental swallowing exams such as videofluoroscopic swallowing studies. videofluoroscopic swallowing studies involve the inspection of a series of radiographic images for signs of swallowing dysfunction. Though effective, videofluoroscopic swallowing studies are only available in certain clinical settings and are not always desirable or feasible for certain patients. Because of the limitations of current instrumental swallow exams, research studies have explored the use of acceleration signals collected from neck sensors and demonstrated their potential in providing comparable radiation-free diagnostic value as videofluoroscopic swallowing studies. In this study, we used a hybrid deep convolutional recurrent neural network that can perform multi-level feature extraction (localized and across time) to annotate swallow segments automatically via multi-channel swallowing acceleration signals. In total, we used signals and videofluoroscopic swallowing study images of 3144 swallows from 248 patients with suspected dysphagia. Compared to other deep network variants, our network was superior at detecting swallow segments with an average area under the receiver operating characteristic curve value of 0.82 (95% confidence interval: 0.807-0.841), and was in agreement with up to 90% of the gold standard-labeled segments.
PDF | DOI: 10.1007/s00034-022-02203-3
This paper addresses the rarely considered issue of hardware implementation of the S-transform, a very important time–frequency representation with many practical applications. Various improved, adaptive, and signal-driven versions of the S-transform have been developed over the years, but only its basic (non-adaptive) form has been implemented in hardware. Here, a novel hardware implementation of the adaptive S-transform is proposed extending the previously developed design. To minimize hardware demands, the proposed approach is based on an appropriate approximation of the frequency window function considered in the S-transform. The adaptivity of the transform to the signal is achieved by an optimal choice of a window parameter from the set of predefined values, meaning that for each window parameter the S-transform is calculated. To additionally save hardware resources, the proposed design does not require storing all calculated values, but only two in each iteration. The proposed multiple-clock-cycle architecture is developed on the field-programmable gate array device, and its performance is compared with other possible implementation approaches such as the hybrid and single-clock-cycle ones. It is demonstrated that the developed design minimizes hardware complexity and clock cycle time compared to alternative approaches and is significantly more efficient than the software realization. Both noiseless and noisy multicomponent highly nonstationary signals were considered. An excellent match between the results of the hardware and the “exact” adaptive S-transform evaluation obtained through the MATLAB implementation is demonstrated. Lastly, the execution time that can be estimated in advance is also an important practical feature of the developed design.
PDF | DOI: 10.1109/JTEHM.2023.3246919
Objective: Dysphagia management relies on the evaluation of the temporospatial kinematic events of swallowing performed in videofluoroscopy (VF) by trained clinicians. The upper esophageal sphincter (UES) opening distension represents one of the important kinematic events that contribute to healthy swallowing. Insufficient distension of UES opening can lead to an accumulation of pharyngeal residue and subsequent aspiration which in turn can lead to adverse outcomes such as pneumonia. VF is usually used for the temporal and spatial evaluation of the UES opening; however, VF is not available in all clinical settings and may be inappropriate or undesirable for some patients. High resolution cervical auscultation (HRCA) is a noninvasive technology that uses neck-attached sensors and machine learning to characterize swallowing physiology by analyzing the swallow-induced vibrations/sounds in the anterior neck region. We investigated the ability of HRCA to noninvasively estimate the maximal distension of anterior-posterior (A-P) UES opening as accurately as the measurements performed by human judges from VF images. Methods and procedures: Trained judges performed the kinematic measurement of UES opening duration and A-P UES opening maximal distension on 434 swallows collected from 133 patients. We used a hybrid convolutional recurrent neural network supported by attention mechanisms which takes HRCA raw signals as input and estimates the value of the A-P UES opening maximal distension as output. Results: The proposed network estimated the A-P UES opening maximal distension with an absolute percentage error of 30% or less for more than 64.14% of the swallows in the dataset. Conclusion: This study provides substantial evidence for the feasibility of using HRCA to estimate one of the key spatial kinematic measurements used for dysphagia characterization and management. Clinical and Translational Impact Statement: The findings in this study have a direct impact on dysphagia diagnosis and management through providing a non-invasive and cheap way to estimate one of the most important swallowing kinematics, the UES opening distension, that contributes to safe swallowing. This study, along with other studies that utilize HRCA for swallowing kinematic analysis, paves the way for developing a widely available and easy-to-use tool for dysphagia diagnosis and management.
PDF | DOI: 10.1016/j.annemergmed.2022.08.005
Study objective: Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis. Methods: This prospective observational cohort study recruited patients with out-of-hospital chest pain. We retrieved out-of-hospital-ECG obtained by paramedics in the field and the first ED ECG obtained by nurses during inhospital evaluation. Two independent and blinded reviewers interpreted ECG dyads in mixed order per practice recommendations. Using 179 morphological ECG features, we trained, cross-validated, and tested a random forest classifier to augment non ST-elevation acute coronary syndrome (NSTE-ACS) diagnosis. Results: Our sample included 2,122 patients (age 59 [16]; 53% women; 44% Black, 13.5% confirmed acute coronary syndrome). The rate of diagnostic ST elevation and ST depression were 5.9% and 16.2% on out-of-hospital-ECG and 6.1% and 12.4% on ED ECG, with ∼40% of changes seen on out-of-hospital-ECG persisting and ∼60% resolving. Using expert interpretation of out-of-hospital-ECG alone gave poor baseline performance with area under the receiver operating characteristic (AUC), sensitivity, and negative predictive values of 0.69, 0.50, and 0.92. Using expert interpretation of serial ECG changes enhanced this performance (AUC 0.80, sensitivity 0.61, and specificity 0.93). Interestingly, augmenting the out-of-hospital-ECG alone with artificial intelligence algorithms boosted its performance (AUC 0.83, sensitivity 0.75, and specificity 0.95), yielding a net reclassification improvement of 29.5% against expert ECG interpretation. Conclusion: In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.
PDF | DOI: 10.1007/s00455-022-10414-8
Clinicians and researchers commonly judge the completeness of hyoid displacement from videofluoroscopic swallow study (VFSS) videos. Judgments made during the clinical exam are often subjective, and post-examination analysis reduces the measure's immediate value. This study aimed to determine the validity and feasibility of a visual, anatomically scaled benchmark for judging complete hyoid displacement during a VFSS. The third and fourth cervical vertebral bodies (C3 and C4) lie at roughly the same vertical position as the hyoid body and are strongly correlated with patient height. We hypothesized that anterior and superior displacement of the hyoid bone would approximate the height of one C3 or C4 body during safe swallows. Trained raters marked points of interest on C3, C4, and the hyoid body on 1414 swallows of adult patients with suspected dysphagia (n = 195) and 50 swallows of age-matched healthy participants (n = 17), and rated Penetration Aspiration Scale scores. Results indicated that the mean displacements of the hyoid bone were greater than one C3 unit in the superior direction for all swallows from patient and healthy participants, though significantly and clinically greater in healthy participant swallows (p less than .001, d > .8). The mean anterior and superior displacements from patient and healthy participant swallows were greater than one C4 unit. Results show preliminary evidence that use of the C3 and/or C4 anatomic scalars can add interpretive value to the immediate judgment of hyoid displacement during the conduct of a clinical VFSS examination.
PDF | DOI: 10.1007/s00455-021-10368-3
There is growing enthusiasm to develop inexpensive, non-invasive, and portable methods that accurately assess swallowing and provide biofeedback during dysphagia treatment. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from non-invasive sensors attached to the anterior laryngeal framework during swallowing, is a novel method for quantifying swallowing physiology via advanced signal processing and machine learning techniques. HRCA has demonstrated potential as a dysphagia screening method and diagnostic adjunct to VFSSs by determining swallowing safety, annotating swallow kinematic events, and classifying swallows between healthy participants and patients with a high degree of accuracy. However, its feasibility as a non-invasive biofeedback system has not been explored. This study investigated 1. Whether HRCA can accurately differentiate between non-effortful and effortful swallows; 2. Whether differences exist in Modified Barium Swallow Impairment Profile (MBSImP) scores (#9, #11, #14) between non-effortful and effortful swallows. We hypothesized that HRCA would accurately classify non-effortful and effortful swallows and that differences in MBSImP scores would exist between the types of swallows. We analyzed 247 thin liquid 3 mL command swallows (71 effortful) to minimize variation from 36 healthy adults who underwent standardized VFSSs with concurrent HRCA. Results revealed differences (p less than 0.05) in 9 HRCA signal features between non-effortful and effortful swallows. Using HRCA signal features as input, decision trees classified swallows with 76% accuracy, 76% sensitivity, and 77% specificity. There were no differences in MBSImP component scores between non-effortful and effortful swallows. While preliminary in nature, this study demonstrates the feasibility/promise of HRCA as a biofeedback method for dysphagia treatment.
PDF | DOI: 10.1109/JBHI.2022.3175862
As different scientific disciplines begin to converge on machine learning for causal inference, we demonstrate the application of machine learning algorithms in the context of longitudinal causal estimation using electronic health records. Our aim is to formulate a marginal structural model for estimating diabetes care provisions in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes: metformin, sulfonylurea and SGLT-2i. The binary outcome of diabetes care provisions was defined using a composite measure of chronic disease prevention and screening elements [27] including (i) primary care visit, (ii) blood pressure, (iii) weight, (iv) hemoglobin A1c, (v) lipid, (vi) ACR, (vii) eGFR and (viii) statin medication. We used several statistical learning algorithms to describe causal relationships between the prescription of three common classes of diabetes medications and quality of diabetes care using the electronic health records contained in National Diabetes Repository. In particular, we generated an ensemble of statistical learning algorithms using the SuperLearner framework based on the following base learners: (i) least absolute shrinkage and selection operator, (ii) ridge regression, (iii) elastic net, (iv) random forest, (v) gradient boosting machines, and (vi) neural network. Each statistical learning algorithm was fitted using the pseudo-population generated from the marginalization of the time-dependent confounding process. Covariate balance was assessed using the longitudinal (i.e. cumulative-time product) stabilized weights with calibrated restrictions. Our results indicated that the treatment drop-in cohorts (with respect to metformin, sulfonylurea and SGLT-2i) may have improved diabetes care provisions in relation to treatment naïve (i.e. no treatment) cohort. As a clinical utility, we hope that this article will facilitate discussions around the prevention of adverse chronic outcomes associated with type II diabetes through the improvement of diabetes care provisions in primary care.
PDF | DOI: 10.1007/s40520-022-02096-x
Real-life mobility, also called "enacted" mobility, characterizes an individual's activity and participation in the community. Real-life mobility may be facilitated or hindered by a variety of factors, such as physical abilities, cognitive function, psychosocial aspects, and external environment characteristics. Advances in technology have allowed for objective quantification of real-life mobility using wearable sensors, specifically, accelerometry and global positioning systems (GPSs). In this review article, first, we summarize the common mobility measures extracted from accelerometry and GPS. Second, we summarize studies assessing the associations of facilitators and barriers influencing mobility of community-dwelling older adults with mobility measures from sensor technology. We found the most used accelerometry measures focus on the duration and intensity of activity in daily life. Gait quality measures, e.g., cadence, variability, and symmetry, are not usually included. GPS has been used to investigate mobility behavior, such as spatial and temporal measures of path traveled, location nodes traversed, and mode of transportation. Factors of note that facilitate/hinder community mobility were cognition and psychosocial influences. Fewer studies have included the influence of external environments, such as sidewalk quality, and socio-economic status in defining enacted mobility. Increasing our understanding of the facilitators and barriers to enacted mobility can inform wearable technology-enabled interventions targeted at delaying mobility-related disability and improving participation of older adults in the community.
PDF | DOI: 10.1016/j.hrthm.2022.02.030
The electrocardiogram (ECG) records the electrical activity in the heart in real time, providing an important opportunity to detecting various cardiac pathologies. The 12-lead ECG currently serves as the "standard" ECG acquisition technique for diagnostic purposes for many cardiac pathologies other than arrhythmias. However, the technical aspects of acquiring a 12-lead ECG are not easy. and its usage currently is restricted to trained medical personnel, which limits the scope of its usefulness. Remote and wearable ECG devices have attempted to bridge this gap by enabling patients to take their own ECG using a simplified method at the expense of a reduced number of leads, usually a single-lead ECG. In this review, we summarize the studies that investigated the use of remote ECG devices and their clinical utility in diagnosing cardiac pathologies. Eligible studies discussed Food and Drug Administration-cleared, commercially available devices that were validated in an adult population. We summarize technical logistics of signal quality and device reliability, dimensional and functional features, and diagnostic value. Our synthesis shows that reduced-set ECG wearables have huge potential for long-term monitoring, particularly if paired with real-time notification techniques. Such capabilities make them primarily useful for abnormal rhythm detection, and there is sufficient evidence that a remote ECG device can be superior to the traditional 12-lead ECG in diagnosing specific arrhythmias such as atrial fibrillation. However, this review identifies important challenges faced by this technology and highlights the limited availability of clinical research examining their usefulness.
PDF | DOI: 10.1007/s00455-021-10317-0
Few research studies have investigated temporal kinematic swallow events in healthy adults to establish normative reference values. Determining cutoffs for normal and disordered swallowing is vital for differentially diagnosing presbyphagia, variants of normal swallowing, and dysphagia; and for ensuring that different swallowing research laboratories produce consistent results in common measurements from different samples within the same population. High-resolution cervical auscultation (HRCA), a sensor-based dysphagia screening method, has accurately annotated temporal kinematic swallow events in patients with dysphagia, but hasn't been used to annotate temporal kinematic swallow events in healthy adults to establish dysphagia screening cutoffs. This study aimed to determine: (1) Reference values for temporal kinematic swallow events, (2) Whether HRCA can annotate temporal kinematic swallow events in healthy adults. We hypothesized (1) Our reference values would align with a prior study; (2) HRCA would detect temporal kinematic swallow events as accurately as human judges. Trained judges completed temporal kinematic measurements on 659 swallows (N = 70 adults). Swallow reaction time and LVC duration weren't different (p > 0.05) from a previously published historical cohort (114 swallows, N = 38 adults), while other temporal kinematic measurements were different (p less than 0.05), suggesting a need for further standardization to feasibly pool data analyses across laboratories. HRCA signal features were used as input to machine learning algorithms and annotated UES opening (69.96% accuracy), UES closure (64.52% accuracy), LVC (52.56% accuracy), and LV re-opening (69.97% accuracy); providing preliminary evidence that HRCA can noninvasively and accurately annotate temporal kinematic measurements in healthy adults to determine dysphagia screening cutoffs.
PDF | DOI: 10.1016/j.ajem.2021.12.071
Background: Identifying older adults with risk for falls prior to discharge home from the Emergency Department (ED) could help direct fall prevention interventions, yet ED-based tools to assist risk stratification are under-developed. The aim of this study was to assess the performance of self-report and functional assessments to predict falls in the 3 months post-ED discharge for older adults. Methods: A prospective cohort of community-dwelling adults age 60 years and older were recruited from one urban ED (N = 134). Participants completed: a single item screen for mobility (SIS-M), the 12-item Stay Independent Questionnaire (SIQ-12), and the Timed Up and Go test (TUG). Falls were defined through self-report of any fall at 1- and 3-months and medical record review for fall-related injury 3-months post-discharge. We developed a hybrid-convolutional recurrent neural network (HCRNN) model of gait and balance characteristics using truncal 3-axis accelerometry collected during the TUG. Internal validation was conducted using bootstrap resampling with 1000 iterations for SIS-M, FRQ, and GUG and leave-one-out for the HCRNN. We compared performance of M-SIS, FRQ, TUG time, and HCRNN by calculating the area under the receiver operating characteristic area under the curves (AUCs). Results: 14 (10.4%) of participants met our primary outcome of a fall or fall-related injury within 3-months. The SIS-M had an AUC of 0.42 [95% confidence interval (CI) 0.19-0.65]. The SIQ-12 score had an AUC of 0.64 [95% confidence interval (CI) 0.49-0.80]. The TUG had an AUC of 0.48 (95% CI 0.29-0.68). The HCRNN model using generated accelerometer features collected during the TUG had an AUC of 0.99 (95% CI 0.98-1.00). Conclusion: We found that self-report and functional assessments lack sufficient accuracy to be used in isolation in the ED. A neural network model using accelerometer features could be a promising modality but research is needed to externally validate these findings.
PDF | DOI: 10.1109/JBHI.2021.3106565
Aspiration is a serious complication of swallowing disorders. Adequate detection of aspiration is essential in dysphagia management and treatment. High-resolution cervical auscultation has been increasingly considered as a promising noninvasive swallowing screening tool and has inspired automatic diagnosis with advanced algorithms. The performance of such algorithms relies heavily on the amount of training data. However, the practical collection of cervical auscultation signal is an expensive and time-consuming process because of the clinical settings and trained experts needed for acquisition and interpretations. Furthermore, the relatively infrequent incidence of severe airway invasion during swallowing studies constrains the performance of machine learning models. Here, we produced supplementary training exemplars for desired class by capturing the underlying distribution of original cervical auscultation signal features using auxiliary classifier Wasserstein generative adversarial networks. A 10-fold subject cross-validation was conducted on 2079 sets of 36-dimensional signal features collected from 189 patients undergoing swallowing examinations. The proposed data augmentation outperforms basic data sampling, cost-sensitive learning and other generative models with significant enhancement. This demonstrates the remarkable potential of proposed network in improving classification performance using cervical auscultation signals and paves the way of developing accurate noninvasive swallowing evaluation in dysphagia care.
PDF | DOI: 10.14309/ajg.0000000000001618
Artificial intelligence (AI) is revolutionizing big data analytics. In this issue of The American Journal of Gastroenterology, Ahn et al. introduce the AI-cirrhosis-electrocardiogram score that can grade the electrophysiologic cardiac changes present in patients with cirrhosis. Apart from showing excellent accuracy to identify cirrhosis, the AI-cirrhosis-electrocardiogram algorithm identified a biological gradient and signal reversibility after transplantation. Clinical applicability needs to be determined. Some concerns inherent to the use of AI are discussed, including the need to verify that the quality of data used for machine training is optimal. The black box nature of AI-identified associations is discussed, along with the lack of pathophysiologic coherence allowing intuitive medical reasoning.
PDF | DOI: 10.1016/j.inffus.2021.09.016
Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal, kinematic and muscle activation gait characteristics are investigated. Additionally, protocols for gait analysis are going beyond labs/clinics to provide more habitual insights, uncovering underlying reasons for limited mobility and falls during daily activities. Wearables are the most prominent technology that are reliable and allow multi-modal gait analysis beyond the labs/clinics for extended periods. There are established wearable-based algorithms for extracting informative gait characteristics and interpretation. This paper proposes a multi-layer fusion framework with sensor, data and gait characteristics. The wearable sensors consist of four units (inertial and electromyography, EMG) attached to both legs (shanks and thighs) and surface electrodes placed on four muscle groups. Inertial and EMG data are interpreted by numerous validated algorithms to extract gait characteristics in different environments. This paper also includes a pilot study to test the proposed fusion approach in a small cohort of stroke survivors. Experimental results in various terrains show healthy participants experienced the highest pace and variability along with slightly increased knee flexion angles (≈1°) and decreased overall muscle activation level during outdoor walking compared to indoor, incline walking activities. Stroke survivors experienced slightly increased pace, asymmetry, and knee flexion angles (≈4°) during outdoor walking compared to indoor. A multi-modal approach through a sensor, data and gait characteristic fusion presents a more holistic gait assessment process to identify changes in different testing environments. The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.
PDF | DOI: 10.3390/s21248428
Dual-task balance studies explore interference between balance and cognitive tasks. This study is a descriptive analysis of accelerometry balance metrics to determine if a verbal cognitive task influences postural control after the task ends. Fifty-two healthy older adults (75 ± 6 years old, 30 female) performed standing balance and cognitive dual-tasks. An accelerometer recorded movement from before, during, and after the task (reciting every other letter of the alphabet). Thirty-six balance metrics were calculated for each task condition. The effect of the cognitive task on postural control was determined by a generalized linear model. Twelve variables, including anterior-posterior centroid frequency, peak frequency and entropy rate, medial-later entropy rate and wavelet entropy, and bandwidth in all directions, exhibited significant differences between baseline and cognitive task periods, but not between baseline and post-task periods. These results indicate that the verbal cognitive task did alter balance, but did not bring about persistent effects after the task had ended. Traditional balance measurements, i.e., root mean square and normalized path length, notably lacked significance, highlighting the potential to use other accelerometer metrics for the early detection of balance problems. These novel insights into the temporal dynamics of dual-task balance support current dual-task paradigms to reduce fall risk in older adults.
PDF | DOI: 10.1109/JTEHM.2021.3134926
Dysphagia, commonly referred to as abnormal swallowing, affects millions of people annually. If not diagnosed expeditiously, dysphagia can lead to more severe complications, such as pneumonia, nutritional deficiency, and dehydration. Bedside screening is the first step of dysphagia characterization and is usually based on pass/fail tests in which a nurse observes the patient performing water swallows to look for dysphagia overt signs such as coughing. Though quick and convenient, bedside screening only provides low-level judgment of impairment, lacks standardization, and suffers from subjectivity. Recently, high resolution cervical auscultation (HRCA) has been investigated as a less expensive and non-invasive method to diagnose dysphagia. It has shown strong preliminary evidence of its effectiveness in penetration-aspiration detection as well as multiple swallow kinematics. HRCA signals have traditionally been collected and investigated in conjunction with videofluoroscopy exams which are performed using barium boluses including thin liquid. An HRCA-based bedside screening is highly desirable to expedite the initial dysphagia diagnosis and overcome all the drawbacks of the current pass/fail screening tests. However, all research conducted for using HRCA in dysphagia is based on thin liquid barium boluses and thus not guaranteed to provide valid results for water boluses used in bedside screening. If HRCA signals show no significant differences between water and thin liquid barium boluses, then the same algorithms developed on thin liquid barium boluses used in diagnostic imaging studies, it can be then directly used with water boluses. This study investigates the similarities and differences between HRCA signals from thin liquid barium swallows compared to those signals from water swallows. Multiple features from the time, frequency, time-frequency, and information-theoretic domain were extracted from each type of swallow and a group of linear mixed models was tested to determine the significance of differences. Machine learning classifiers were fit to the data as well to determine if the swallowed material (thin liquid barium or water) can be correctly predicted from an unlabeled set of HRCA signals. The results demonstrated that there is no systematic difference between the HRCA signals of thin liquid barium swallows and water swallows. While no systematic difference was discovered, the evidence of complete conformity between HRCA signals of both materials was inconclusive. These results must be validated further to confirm conformity between the HRCA signals of thin liquid barium swallows and water swallows.
PDF | DOI: 10.1016/j.media.2021.102218
Judging swallowing kinematic impairments via videofluoroscopy represents the gold standard for the detection and evaluation of swallowing disorders. However, the efficiency and accuracy of such a biomechanical kinematic analysis vary significantly among human judges affected mainly by their training and experience. Here, we showed that a novel machine learning algorithm can with high accuracy automatically detect key anatomical points needed for a routine swallowing assessment in real-time. We trained a novel two-stage convolutional neural network to localize and measure the vertebral bodies using 1518 swallowing videofluoroscopies from 265 patients. Our network model yielded high accuracy as the mean distance between predicted points and annotations was 4.20 5.54 pixels. In comparison, human inter-rater error was 4.35 3.12 pixels. Furthermore, 93% of predicted points were less than five pixels from annotated pixels when tested on an independent dataset from 70 subjects. Our model offers more choices for speech language pathologists in their routine clinical swallowing assessments as it provides an efficient and accurate method for anatomic landmark localization in real-time, a task previously accomplished using an off-line time-sinking procedure.
PDF | DOI: 10.1016/j.jelectrocard.2021.07.012
Background: Novel temporal-spatial features of the 12‑lead ECG can conceptually optimize culprit lesions' detection beyond that of classical ST amplitude measurements. We sought to develop a data-driven approach for ECG feature selection to build a clinically relevant algorithm for real-time detection of culprit lesion. Methods: This was a prospective observational cohort study of chest pain patients transported by emergency medical services to three tertiary care hospitals in the US. We obtained raw 10-s, 12‑lead ECGs (500 s/s, HeartStart MRx, Philips Healthcare) during prehospital transport and followed patients 30 days after the encounter to adjudicate clinical outcomes. A total of 557 global and lead-specific features of P-QRS-T waveform were harvested from the representative average beats. We used Recursive Feature Elimination and LASSO to identify 35/557, 29/557, and 51/557 most recurrent and important features for LAD, LCX, and RCA culprits, respectively. Using the union of these features, we built a random forest classifier with 10-fold cross-validation to predict the presence or absence of culprit lesions. We compared this model to the performance of a rule-based commercial proprietary software (Philips DXL ECG Algorithm). Results: Our sample included 2400 patients (age 59 ± 16, 47% female, 41% Black, 10.7% culprit lesions). The area under the ROC curves of our random forest classifier was 0.85 ± 0.03 with sensitivity, specificity, and negative predictive value of 71.1%, 84.7%, and 96.1%. This outperformed the accuracy of the automated interpretation software of 37.2%, 95.6%, and 92.7%, respectively, and corresponded to a net reclassification improvement index of 23.6%. Metrics of ST80; Tpeak-Tend; spatial angle between QRS and T vectors; PCA ratio of STT waveform; T axis; and QRS waveform characteristics played a significant role in this incremental gain in performance. Conclusions: Novel computational features of the 12‑lead ECG can be used to build clinically relevant machine learning-based classifiers to detect culprit lesions, which has important clinical implications.
PDF | DOI: 10.1093/gerona/glab151
Background: The relation of gait quality to real-life mobility among older adults is poorly understood. This study examined the association between gait quality, consisting of step variability, smoothness, regularity, symmetry, and gait speed, and the Life-Space Assessment (LSA). Method: In community-dwelling older adults (N = 232, age 77.5 ± 6.6, 65% females), gait quality was derived from (i) an instrumented walkway: gait speed, variability, and walk ratio and (ii) accelerometer: signal variability, smoothness, regularity, symmetry, and time-frequency spatiotemporal variables during 6-minute walk. In addition to collecting LSA scores, cognitive functioning, walking confidence, and falls were recorded. Spearman correlations (speed as covariate) and random forest regression were used to assess associations between gait quality and LSA, and Gaussian mixture modeling (GMM) was used to cluster participants. Results: Spearman correlations of ρ p = .11 (signal amplitude variability mediolateral [ML] axis), ρ p = .15 and ρ p = -.13 (symmetry anterior-posterior-vertical [AP-V] and ML-AP axes, respectively), ρ p = .16 (power V), and ρ = .26 (speed), all p less than .05 and marginally related, ρ p = -.12 (regularity V), ρ p = .11 (smoothness AP), and ρ p = -.11 (step-time variability), all p less than .1, were obtained. The cross-validated random forest model indicated good-fit LSA prediction error of 17.77; gait and cognition were greater contributors than age and gender. GMM indicated 2 clusters. Group 1 (n = 189) had better gait quality than group 2 (n = 43): greater smoothness AP (2.94 ± 0.75 vs 2.30 ± 0.71); greater similarity AP-V (.58 ± .13 vs .40 ± .19); lower regularity V (0.83 ± 0.08 vs 0.87 ± 0.10); greater power V (1.86 ± 0.18 vs 0.97 ± 1.84); greater speed (1.09 ± 0.16 vs 1.00 ± 0.16 m/s); lower step-time coefficient of variation (3.70 ± 1.09 vs 5.09 ± 2.37), and better LSA (76 ± 18 vs 67 ± 18), padjusted less than .004. Conclusions: Gait quality measures taken in the clinic are associated with real-life mobility in the community.
PDF | DOI: 10.1093/ageing/afab076
Background: falls and fall-related injuries are common in older adults, have negative effects both on quality of life and functional independence and are associated with increased morbidity, mortality and health care costs. Current clinical approaches and advice from falls guidelines vary substantially between countries and settings, warranting a standardised approach. At the first World Congress on Falls and Postural Instability in Kuala Lumpur, Malaysia, in December 2019, a worldwide task force of experts in falls in older adults, committed to achieving a global consensus on updating clinical practice guidelines for falls prevention and management by incorporating current and emerging evidence in falls research. Moreover, the importance of taking a person-centred approach and including perspectives from patients, caregivers and other stakeholders was recognised as important components of this endeavour. Finally, the need to specifically include recent developments in e-health was acknowledged, as well as the importance of addressing differences between settings and including developing countries. Methods: a steering committee was assembled and 10 working Groups were created to provide preliminary evidence-based recommendations. A cross-cutting theme on patient's perspective was also created. In addition, a worldwide multidisciplinary group of experts and stakeholders, to review the proposed recommendations and to participate in a Delphi process to achieve consensus for the final recommendations, was brought together. Conclusion: in this New Horizons article, the global challenges in falls prevention are depicted, the goals of the worldwide task force are summarised and the conceptual framework for development of a global falls prevention and management guideline is presented.
PDF | DOI: 10.1109/TNSRE.2020.3044260
In adults 65 years or older, falls or other neuromotor dysfunctions are often framed as walking-related declines in motor skill; the frequent occurrence of such decline in walking-related motor skill motivates the need for an improved understanding of the motor skill of walking. Simple gait measurements, such as speed, do not provide adequate information about the quality of the body motion's translation during walking. Gait measures from accelerometers can enrich measurements of walking and motor performance. This review article will categorize the aspects of the motor skill of walking and review how trunk-acceleration gait measures during walking can be mapped to motor skill aspects, satisfying a clinical need to understand how well accelerometer measures assess gait. We will clarify how to leverage more complicated acceleration measures to make accurate motor skill decline predictions, thus furthering fall research in older adults.
PDF | DOI: 10.1044/2021_JSLHR-21-00134
Purpose: The prevalence of dysphagia in patients with neurodegenerative diseases (ND) is alarmingly high and frequently results in morbidity and accelerated mortality due to subsequent adverse events (e.g., aspiration pneumonia). Swallowing in patients with ND should be continuously monitored due to the progressive disease nature. Access to instrumental swallow evaluations can be challenging, and limited studies have quantified changes in temporal/spatial swallow kinematic measures in patients with ND. High-resolution cervical auscultation (HRCA), a dysphagia screening method, has accurately differentiated between safe and unsafe swallows, identified swallow kinematic events (e.g., laryngeal vestibule closure [LVC]), and classified swallows between healthy adults and patients with ND. This study aimed to (a) compare temporal/spatial swallow kinematic measures between patients with ND and healthy adults and (b) investigate HRCA's ability to annotate swallow kinematic events in patients with ND. We hypothesized there would be significant differences in temporal/spatial swallow measurements between groups and that HRCA would accurately annotate swallow kinematic events in patients with ND. Method: Participants underwent videofluoroscopic swallowing studies with concurrent HRCA. We used linear mixed models to compare temporal/spatial swallow measurements (n = 170 ND patient swallows, n = 171 healthy adult swallows) and deep learning machine-learning algorithms to annotate specific temporal and spatial kinematic events in swallows from patients with ND. Results: Differences (p less than .05) were found between groups for several temporal and spatial swallow kinematic measures. HRCA signal features were used as input to machine-learning algorithms and annotated upper esophageal sphincter (UES) opening, UES closure, LVC, laryngeal vestibule reopening, and hyoid bone displacement with 66.25%, 85%, 68.18%, 70.45%, and 44.6% accuracy, respectively, compared to human judges' measurements. Conclusion: This study demonstrates HRCA's potential in characterizing swallow function in patients with ND and other patient populations.
PDF | DOI: 10.1007/s00455-020-10191-2
Clinicians evaluate swallow kinematic events by analyzing videofluoroscopy (VF) images for dysphagia management. The duration of upper esophageal sphincter opening (DUESO) is one important temporal swallow event, because reduced DUESO can result in pharyngeal residue and penetration/aspiration. VF is frequently used for evaluating swallowing but exposes patients to radiation and is not always feasible/readily available. High resolution cervical auscultation (HRCA) is a non-invasive, sensor-based dysphagia screening method that uses signal processing and machine learning to characterize swallowing. We investigated HRCA’s ability to annotate DUESO and predict Modified Barium Swallow Impairment Profile (MBSImP) scores (component #14). We hypothesized that HRCA and machine learning techniques would detect DUESO with similar accuracy as human judges. Trained judges completed temporal kinematic measurements of DUESO on 719 swallows (116 patients) and 50 swallows (15 age-matched healthy adults). An MBSImP certified clinician completed MBSImP ratings on 100 swallows. A multi-layer convolutional recurrent neural network (CRNN) using HRCA signal features for input was used to detect DUESO. Generalized estimating equations models were used to determine statistically significant HRCA signal features for predicting DUESO MBSImP scores. A support vector machine (SVM) classifier and a leave-one-out procedure was used to predict DUESO MBSImP scores. The CRNN detected UES opening within a 3-frame tolerance for 82.6% of patient and 86% of healthy swallows and UES closure for 72.3% of patient and 64% of healthy swallows. The SVM classifier predicted DUESO MBSImP scores with 85.7% accuracy. This study provides evidence of HRCA’s feasibility in detecting DUESO without VF images.
PDF | DOI: 10.1007/s00455-020-10177-0
High-resolution cervical auscultation (HRCA) is an emerging method for non-invasively assessing swallowing by using acoustic signals from a contact microphone, vibratory signals from an accelerometer, and advanced signal processing and machine learning techniques. HRCA has differentiated between safe and unsafe swallows, predicted components of the Modified Barium Swallow Impairment Profile, and predicted kinematic events of swallowing such as hyoid bone displacement, laryngeal vestibular closure, and upper esophageal sphincter opening with a high degree of accuracy. However, HRCA has not been used to characterize swallow function in specific patient populations. This study investigated the ability of HRCA to differentiate between swallows from healthy people and people with neurodegenerative diseases. We hypothesized that HRCA would differentiate between swallows from healthy people and people with neurodegenerative diseases with a high degree of accuracy. We analyzed 170 swallows from 20 patients with neurodegenerative diseases and 170 swallows from 51 healthy age-matched adults who underwent concurrent video fluoroscopy with non-invasive neck sensors. We used a linear mixed model and several supervised machine learning classifiers that use HRCA signal features and a leave-one-out procedure to differentiate between swallows. Twenty-two HRCA signal features were statistically significant (p less than 0.05) for predicting whether swallows were from healthy people or from patients with neurodegenerative diseases. Using the HRCA signal features alone, logistic regression and decision trees classified swallows between the two groups with 99% accuracy, 100% sensitivity, and 99% specificity. This provides preliminary research evidence that HRCA can differentiate swallow function between healthy and patient populations.
PDF | DOI: 10.1016/j.cmpb.2021.106104
Background and Objective: Human walking is typically assessed using a sensor placed on the lower back or the hip. Such analyses often ignore that the arms, legs, and body trunk movements all have significant roles during walking; in other words, these body nodes with accelerometers form a body sensor network (BSN). BSN refers to a network of wearable sensors or devices on the human body that collects physiological signals. Our study proposes that human locomotion could be considered as a network of well-connected nodes. Methods: While hypothesizing that accelerometer data can model this BSN, we collected accelerometer signals from six body areas from ten healthy participants performing a cognitive task. Machine learning based on genetic programming was used to produce a collection of non-linear symbolic models of human locomotion. Results: With implications in precision medicine, our primary finding was that our BSN models fit the data from the lower back's accelerometer and describe subject-specific data the best compared to all other models. Across subjects, models were less effective due to the diversity of human sizes. Conclusions: A BSN relationship between all six body nodes has been shown to describe the subject-specific data, which indicates that the network-medicine relationship between these nodes is essential in adequately describing human walking. Our gait analyses can be used for several clinical applications such as medical diagnostics as well as creating a baseline for healthy walking with and without a cognitive load.
PDF | DOI: 10.1007/s42452-021-04632-2
Swallowing physiology includes numerous biomechanical events including displacement of the hyoid bone, which is a crucial component of airway protection and opening of the proximal esophagus. The objective of this study was to evaluate the potential relations between the trajectory of hyoid bone movement and the risk of airway penetration and aspiration during a videofluoroscopic swallowing study. Two hundred sixty-five patients were involved in this study, producing a total of 1433 swallows of various volumes consisting of thin liquid, nectar-thick liquid, and solids during a fluoroscopic exam. The anterior and posterior landmarks of the body of the hyoid bone were manually marked in each frame of each fluoroscopic video. Generalized estimation equations were applied to evaluate the relationship between penetration–aspiration scores and mathematical features extracted from the hyoid bone trajectories, while also considering the influence of other independent variables such as age, bolus volume, and viscosity. Our results indicated that penetration–aspiration scores showed a significant relation to age. The maximum anterior (horizontal) displacement of the anterior hyoid bone landmark was significantly associated with the penetration–aspiration scores. Differences in the displacement of the hyoid bone are useful observations in airway protection.
PDF | DOI: 10.1007/s00455-020-10124-z
Identifying physiological impairments of swallowing is essential for determining accurate diagnosis and appropriate treatment for patients with dysphagia. The hyoid bone is an anatomical landmark commonly monitored during analysis of videofluoroscopic swallow studies (VFSSs). Its displacement is predictive of penetration/aspiration and is associated with other swallow kinematic events. However, VFSSs are not always readily available/feasible and expose patients to radiation. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from a microphone and tri-axial accelerometer, is under investigation as a non-invasive dysphagia screening method and potential adjunct to VFSS when it is unavailable or not feasible. We investigated the ability of HRCA to independently track hyoid bone displacement during swallowing with similar accuracy to VFSS, by analyzing vibratory signals from a tri-axial accelerometer using machine learning techniques. We hypothesized HRCA would track hyoid bone displacement with a high degree of accuracy compared to humans. Trained judges completed frame-by-frame analysis of hyoid bone displacement on 400 swallows from 114 patients and 48 swallows from 16 age-matched healthy adults. Extracted features from vibratory signals were used to train the predictive algorithm to generate a bounding box surrounding the hyoid body on each frame. A metric of relative overlapped percentage (ROP) compared human and machine ratings. The mean ROP for all swallows analyzed was 50.75%, indicating > 50% of the bounding box containing the hyoid bone was accurately predicted in every frame. This provides evidence of the feasibility of accurate, automated hyoid bone displacement tracking using HRCA signals without use of VFSS images.
PDF | DOI: 10.1021/acssensors.0c01973
Acetone is a metabolic byproduct found in the exhaled breath and can be measured to monitor the metabolic degree of ketosis. In this state, the body uses free fatty acids as its main source of fuel because there is limited access to glucose. Monitoring ketosis is important for type I diabetes patients to prevent ketoacidosis, a potentially fatal condition, and individuals adjusting to a low-carbohydrate diet. Here, we demonstrate that a chemiresistor fabricated from oxidized single-walled carbon nanotubes functionalized with titanium dioxide (SWCNT@TiO2) can be used to detect acetone in dried breath samples. Initially, due to the high cross sensitivity of the acetone sensor to water vapor, the acetone sensor was unable to detect acetone in humid gas samples. To resolve this cross-sensitivity issue, a dehumidifier was designed and fabricated to dehydrate the breath samples. Sensor response to the acetone in dried breath samples from three volunteers was shown to be linearly correlated with the two other ketone bodies, acetoacetic acid in urine and β-hydroxybutyric acid in the blood. The breath sampling and analysis methodology had a calculated acetone detection limit of 1.6 ppm and capable of detecting up to at least 100 ppm of acetone, which is the dynamic range of breath acetone for someone with ketosis. Finally, the application of the sensor as a breath acetone detector was studied by incorporating the sensor into a handheld prototype breathalyzer.
PDF | DOI: 10.1088/1361-6579/abe7cb
Objective. Adequate upper esophageal sphincter (UES) opening is essential during swallowing to enable clearance of material into the digestive system, and videofluoroscopy (VF) is the most commonly deployed instrumental examination for assessment of UES opening. High-resolution cervical auscultation (HRCA) has been shown to be an effective, portable and cost-efficient screening tool for dysphagia with strong capabilities in non-invasively and accurately approximating manual measurements of VF images. In this study, we aimed to examine whether the HRCA signals are correlated to the manually measured anterior–posterior (AP) distension of maximal UES opening from VF recordings, under the hypothesis that they would be strongly associated. Approach. We developed a standardized method to spatially measure the AP distension of maximal UES opening in 203 swallows VF recording from 27 patients referred for VF due to suspected dysphagia. Statistical analysis was conducted to compare the manually measured AP distension of maximal UES opening from lateral plane VF images and features extracted from two sets of HRCA signal segments: whole swallow segments and segments excluding all events other than the duration of UES is opening. Main results. HRCA signal features were significantly associated with the normalized AP distension of the maximal UES opening in the longer whole swallowing segments and the association became much stronger when analysis was performed solely during the duration of UES opening. Significance. This preliminary feasibility study demonstrated the potential value of HRCA signals features in approximating the objective measurements of maximal UES AP distension and paves the way of developing HRCA to non-invasively and accurately predict human spatial measurement of VF kinematic events.
PDF | DOI: 10.1016/j.future.2020.09.040
Laryngeal vestibule (LV) closure is a critical physiologic event during swallowing, since it is the first line of defense against food bolus entering the airway. Identifying the laryngeal vestibule status, including closure, reopening and closure duration, provides indispensable references for assessing the risk of dysphagia and neuromuscular function. However, commonly used radiographic examinations, known as videofluoroscopy swallowing studies, are highly constrained by their radiation exposure and cost. Here, we introduce a non-invasive sensor-based system, that acquires high-resolution cervical auscultation signals from neck and accommodates advanced deep learning techniques for the detection of LV behaviors. The deep learning algorithm, which combined convolutional and recurrent neural networks, was developed with a dataset of 588 swallows from 120 patients with suspected dysphagia and further clinically tested on 45 samples from 16 healthy participants. For classifying the LV closure and opening statuses, our method achieved 78.94% and 74.89% accuracies for these two datasets, suggesting the feasibility of implementing sensor signals for LV prediction without traditional videofluoroscopy screening methods. The sensor supported system offers a broadly applicable computational approach for clinical diagnosis and biofeedback purposes in patients with swallowing disorders without the use of radiographic examination.
PDF | DOI: 10.1161/JAHA.120.017871
Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications.
PDF | DOI: 10.1109/JBHI.2020.3000057
Upper esophageal sphincter is an important anatomical landmark of the swallowing process commonly observed through the kinematic analysis of radiographic examinations that are vulnerable to subjectivity and clinical feasibility issues. Acting as the doorway of esophagus, upper esophageal sphincter allows the transition of ingested materials from pharyngeal into esophageal stages of swallowing and a reduced duration of opening can lead to penetration/aspiration and/or pharyngeal residue. Therefore, in this study we consider a non-invasive high resolution cervical auscultation-based screening tool to approximate the human ratings of upper esophageal sphincter opening and closure. Swallows were collected from 116 patients and a deep neural network was trained to produce a mask that demarcates the duration of upper esophageal sphincter opening. The proposed method achieved more than 90% accuracy and similar values of sensitivity and specificity when compared to human ratings even when tested over swallows from an independent clinical experiment. Moreover, the predicted opening and closure moments surprisingly fell within an inter-human comparable error of their human rated counterparts which demonstrates the clinical significance of high resolution cervical auscultation in replacing ionizing radiation-based evaluation of swallowing kinematics.
PDF | DOI: 10.1590/S0004-2803.202000000-66
Dysphagia management, from screening procedures to diagnostic methods and therapeutic approaches, is about to change dramatically. This change is prompted not solely by great discoveries in medicine or physiology, but by advances in electronics and data science and close collaboration and cross-pollination between these two disciplines. In this editorial, we will provide a brief overview of the role of artificial intelligence in dysphagia management.
PDF | DOI: 10.1016/j.dsp.2020.102802
Graph signal processing deals with signals which are observed on an irregular graph domain. While many approaches have been developed in classical graph theory to cluster vertices and segment large graphs in a signal independent way, signal localization based approaches to the analysis of data on graph represent a new research direction which is also a key to big data analytics on graphs. To this end, after an overview of the basic definitions of graphs and graph signals, we present and discuss a localized form of the graph Fourier transform. To establish analogy with classical signal processing, spectral domain and vertex domain definitions of the localization window are given next. The spectral and vertex localization kernels are then related to the wavelet transform, followed by their polynomial approximations and a study of filtering and inversion operations. For rigor, the analysis of energy representation and frames in the localized graph Fourier transform is extended to the energy forms of vertex-frequency distributions, which operate even without the requirement to apply localization windows. Another link with classical signal processing is established through the concept of local smoothness, which is subsequently related to the paradigm of signal smoothness on graphs, a lynchpin which connects the properties of the signals on graphs and graph topology. This all represents a comprehensive account of the relation of general vertex-frequency analysis with classical time-frequency analysis, an important but missing link for more advanced applications of graph signal processing. The theory is supported by illustrative and practically relevant examples.
Purpose: Safe swallowing requires adequate protection of the airway to prevent swallowed materials from entering the trachea or lungs (i.e., aspiration). Laryngeal vestibular closure (LVC) is the first line of defense against swallowed material entering the airway. Absent LVC or mistimed/shortened closure duration can lead to aspiration, adverse medical consequences, and even death. Laryngeal vestibular closure mechanisms can be judged commonly through the videofluoroscopic swallowing (VFS) study, however, this type of instrumentation exposes patients to radiation and is not available or acceptable to all patients. There is growing interest in noninvasive methods to assess/monitor swallow physiology. In this study, we hypothesized that our non-invasive sensor-based system, which has been shown to accurately track hyoid displacement and upper esophageal sphincter opening duration during swallowing, could predict laryngeal vestibular status, including the onset of LVC and laryngeal vestibular re-opening (LVO) in real time and estimate the closure duration with a comparable degree of accuracy as trained human raters. Methods: The sensor-based system used in this study is high-resolution cervical auscultation (HRCA). Advanced machine learning techniques enable HRCA signal analysis through feature extraction and complex algorithms. A deep learning model was developed with a dataset of 588 swallows from 120 patients with suspected dysphagia and further tested on 45 swallows from 16 healthy participants. Results: The new technique achieved an overall mean accuracy of 74.90% and 75.48%, for the two data sets respectively, in distinguishing LVC status. Closure duration ratios between automated and gold-standard human judgment of LVC duration were 1.13 for the patient data set and 0.93 for the healthy participant data set. Conclusion: This study found that HRCA signal analysis using advanced machine learning techniques can effectively predict LV status (closure or opening) and further estimate LVC duration. HRCA is potentially a non-invasive tool to estimate LVC duration for diagnostic and biofeedback purposes without x-ray imaging.
PDF | DOI: 10.1097/JCN.0000000000000644
Background: The Emergency Severity Index (ESI) is a widely used tool to triage patients in emergency departments. The ESI tool is used to assess all complaints and has significant limitation for accurately triaging patients with suspected acute coronary syndrome (ACS). Objective: We evaluated the accuracy of ESI in predicting serious outcomes in suspected ACS and aimed to assess the incremental reclassification performance if ESI is supplemented with a clinically validated tool used to risk-stratify suspected ACS. Methods: We used existing data from an observational cohort study of patients with chest pain. We extracted ESI scores documented by triage nurses during routine medical care. Two independent reviewers adjudicated the primary outcome, incidence of 30-day major adverse cardiac events. We compared ESI with the well-established modified HEAR/T (patient History, Electrocardiogram, Age, Risk factors, but without Troponin) score. Results: Our sample included 750 patients (age, 59 ± 17 years; 43% female; 40% black). A total of 145 patients (19%) experienced major adverse cardiac event. The area under the receiver operating characteristic curve for ESI score for predicting major adverse cardiac event was 0.656, compared with 0.796 for the modified HEAR/T score. Using the modified HEAR/T score, 181 of the 391 false positives (46%) and 16 of the 19 false negatives (84%) assigned by ESI could be reclassified correctly. Conclusion: The ESI score is poorly associated with serious outcomes in patients with suspected ACS. Supplementing the ESI tool with input from other validated clinical tools can greatly improve the accuracy of triage in patients with suspected ACS.
PDF | DOI: 10.1016/j.bpsc.2019.06.010
Background: Little is known about neural oscillatory dynamics in first-episode psychosis. Pathophysiology of functional connectivity can be measured through network activity of alpha oscillations, reflecting long-range communication between distal brain regions. Methods: Resting magnetoencephalographic activity was collected from 31 individuals with first-episode schizophrenia spectrum psychosis and 22 healthy control individuals. Activity was projected to the realistic cortical surface, based on structural magnetic resonance imaging. The first principal component of activity in 40 Brodmann areas per hemisphere was Hilbert transformed within the alpha range. Non-negative matrix factorization was applied to single-trial alpha phase-locking values from all subjects to determine alpha networks. Within networks, energy and entropy were compared. Results: Four cortical alpha networks were pathological in individuals with first-episode schizophrenia spectrum psychosis. The networks involved the bilateral anterior and posterior cingulate; left auditory, medial temporal, and cingulate cortex; right inferior frontal gyrus and widespread areas; and right posterior parietal cortex and widespread areas. Energy and entropy were associated with the Positive and Negative Syndrome Scale total and thought disorder factors for the first three networks. In addition, the left posterior temporal network was associated with positive and negative factors, and the right inferior frontal network was associated with the positive factor. Conclusions: Machine learning network analysis of resting alpha-band neural activity identified several aberrant networks in individuals with first-episode schizophrenia spectrum psychosis, including the left temporal, right inferior frontal, right posterior parietal, and bilateral cingulate cortices. Abnormal long-range alpha communication is evident at the first presentation for psychosis and may provide clues about mechanisms of dysconnectivity in psychosis and novel targets for noninvasive brain stimulation.
PDF | DOI: 10.1038/s41467-020-17804-2
Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain.
PDF | DOI: 10.1002/nur.22045
Emergency department (ED) nurses need to identify patients with potential acute coronary syndrome (ACS) rapidly because treatment delay could impact patient outcomes. Aims of this secondary analysis were to identify key patient factors that could be available at initial ED nurse triage that predict ACS. Consecutive patients with chest pain who called 9‐1‐1, received a 12‐lead electrocardiogram in the prehospital setting, and were transported via emergency medical service were included in the study. A total of 750 patients were recruited. The sample had an average age of 59 years old, was 57% male, and 40% Black. One hundred and fifteen patients were diagnosed with ACS. Older age, non‐Caucasian race, and faster respiratory rate were independent predictors of ACS. There was an interaction between heart rate by Type II diabetes receiving insulin in the context of ACS. Type II diabetics requiring insulin for better glycemic control manifested a faster heart rate. By identifying patient factors at ED nurse triage that could be predictive of ACS, accuracy rates of triage may improve, thus impacting patient outcomes.
PDF | DOI: 10.1044/2020_AJSLP-19-00155
High-resolution cervical auscultation (HRCA) is an evolving clinical method for noninvasive screening of dysphagia that relies on data science, machine learning, and wearable sensors to investigate the characteristics of disordered swallowing function in people with dysphagia. HRCA has shown promising results in categorizing normal and disordered swallowing (i.e., screening) independent of human input, identifying a variety of swallowing physiological events as accurately as trained human judges. The system has been developed through a collaboration of data scientists, computer–electrical engineers, and speech-language pathologists. Its potential to automate dysphagia screening and contribute to evaluation lies in its noninvasive nature (wearable electronic sensors) and its growing ability to accurately replicate human judgments of swallowing data typically formed on the basis of videofluoroscopic imaging data. Potential contributions of HRCA when videofluoroscopic swallowing study may be unavailable, undesired, or not feasible for many patients in various settings are discussed, along with the development and capabilities of HRCA. The use of technological advances and wearable devices can extend the dysphagia clinician's reach and reinforce top-of-license practice for patients with swallowing disorders.
PDF | DOI: 10.15288/jsad.2020.81.505
Objective: Sensing the effects of alcohol consumption in real time could offer numerous opportunities to reduce related harms. This study sought to explore accuracy of gait-related features measured by smartphone accelerometer sensors on detecting alcohol intoxication (breath alcohol concentration [BrAC] > .08%). Method: In a controlled laboratory study, participants (N = 17; 12 male) were asked to walk 10 steps in a straight line, turn, and walk 10 steps back before drinking and each hour, for up to 7 hours after drinking a weight-based dose of alcohol to reach a BrAC of .20%. Smartphones were placed on the lumbar region and 3-axis accelerometer data was recorded at a rate of 100 Hz. Accelerometer data were segmented into task segments (i.e., walk forward, walk backward). Features were generated for each overlapping 1-second windows, and the data set was split into training and testing data sets. Logistic regression models were used to estimate accuracy for classifying BrAC ≤ .08% from BrAC > .08% for each subject. Results: Across participants, BrAC > .08% was predicted with a mean accuracy of 92.5% using logistic regression, an improvement from a naive model accuracy of 88.2% (mean sensitivity = .89; specificity = .92; positive predictive value = .77; and negative predictive value = .97). The two most informative accelerometer features were mean signal amplitude and variance of the signal in the x-axis (i.e., gait sway). Conclusions: We found preliminary evidence supporting use of gait-related features measured by smartphone accelerometer sensors to detect alcohol intoxication. Future research should determine whether these findings replicate in situ.
PDF | DOI: 10.1038/s41598-020-65492-1
High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervical auscultation is a key step for swallowing analysis to be clinically effective. Using time-varying spectral estimation of swallowing signals and deep feed forward neural networks, we propose an automatic segmentation algorithm for swallowing accelerometry and sounds that works directly on the raw swallowing signals in an online fashion. The algorithm was validated qualitatively and quantitatively using the swallowing data collected from 248 patients, yielding over 3000 swallows manually labeled by experienced speech language pathologists. With a detection accuracy that exceeded 95%, the algorithm has shown superior performance in comparison to the existing algorithms and demonstrated its generalizability when tested over 76 completely unseen swallows from a different population. The proposed method is not only of great importance to any subsequent swallowing signal analysis steps, but also provides an evidence that such signals can capture the physiological signature of the swallowing process.
PDF | DOI: 10.1007/s00455-019-10000-5
Videofluoroscopic swallow studies are widely used in clinical and research settings to assess swallow function and to determine physiological impairments, diet recommendations, and treatment goals for people with dysphagia. Videofluoroscopy can be used to analyze biomechanical events of swallowing, including hyoid bone displacement, to differentiate between normal and disordered swallow functions. Previous research has found significant associations between hyoid bone displacement and penetration/aspiration during swallowing, but the predictive value of hyoid bone displacement during swallowing has not been explored. The primary objective of this study was to build a model based on aspects of hyoid bone displacement during swallowing to predict the extent of airway penetration or aspiration during swallowing. Aspects of hyoid bone displacement from 1433 swallows from patients referred for videofluoroscopy were analyzed to determine which aspects predicted risk of penetration and aspiration according to the Penetration–Aspiration Scale. A generalized estimating equation incorporating components of hyoid bone displacement and variables shown to impact penetration and aspiration (such as age, bolus volume, and viscosity) was used to evaluate penetration and aspiration risk. Results indicated that anterior-horizontal hyoid bone displacement was the only aspect of hyoid bone displacement predictive of penetration and aspiration risk. Further research should focus on improving the model performance by identifying additional physiological swallowing events that predict penetration and aspiration risk. The model built for this study, and future modified models, will be beneficial for clinicians to use in the assessment and treatment of people with dysphagia, and for potentially tracking improvement in hyolaryngeal excursion resulting from dysphagia treatment, thus mitigating adverse outcomes that can occur secondary to dysphagia.
PDF | DOI: 10.1109/MSP.2019.2929832
Graphs are irregular structures that naturally represent the multifaceted data attributes; however, traditional approaches have been established outside signal processing and largely focus on analyzing the underlying graphs rather than signals on graphs. Given the rapidly increasing availability of multisensor and multinode measurements, likely recorded on irregular or ad hoc grids, it would be extremely advantageous to analyze such structured data as "signals on graphs" and thus benefit from the ability of graphs to incorporate spatial sensing awareness, physical intuition, and sensor importance, together with the inherent "local versus global" sensor association. The aim of this lecture note is, therefore, to establish a common language between graph signals that are observed in irregular signal domains and some of the most fundamental paradigms in digital signal processing (DSP), such as spectral analysis, system transfer function, digital filter design, parameter estimation, and optimal denoising.
PDF | DOI: 10.1109/TNSRE.2019.2935302
Recent publications have suggested that high-resolution cervical auscultation (HRCA) signals may provide an alternative non-invasive option for swallowing assessment. However, the relationship between hyoid bone displacement, a key component to safe swallowing, and HRCA signals is not thoroughly understood. Therefore, in this work we investigated the hypothesis that a strong relationship exists between hyoid displacement and HRCA signals. Videofuoroscopy data was collected for 129 swallows, simultaneously with vibratory/acoustic signals. Horizontal, vertical and hypotenuse displacements of the hyoid bone were measured through manual expert analysis of videofluoroscopy images. Our results showed that the vertical displacement of both the anterior and posterior landmarks of the hyoid bone was strongly associated with the Lempel-Ziv complexity of superior-inferior and anterior-posterior vibrations from HRCA signals. Horizontal and hypotenuse displacements of the posterior aspect of the hyoid bone were strongly associated with the standard deviation of swallowing sounds. Medial-Lateral vibrations and patient characteristics such as age, sex, and history of stroke were not significantly associated with the hyoid bone displacement. The results imply that some vibratory/acoustic features extracted from HRCA recordings can provide information about the magnitude and direction of hyoid bone displacement. These results provide additional support for using HRCA as a non-invasive tool to assess physiological aspects of swallowing such as the hyoid bone displacement.
PDF | DOI: 10.1021/acssensors.9b00762
Semiconductor-enriched single-walled carbon nanotubes (s-SWCNTs) have potential for application as a chemiresistor for the detection of breath compounds, including tetrahydrocannabinol (THC), the main psychoactive compound found in the marijuana plant. Herein we show that chemiresistor devices fabricated from s-SWCNT ink using dielectrophoresis can be incorporated into a hand-held breathalyzer with sensitivity toward THC generated from a bubbler containing analytical standard in ethanol and a heated sample evaporator that releases compounds from steel wool. The steel wool was used to capture THC from exhaled marijuana smoke. The generation of the THC from the bubbler and heated breath sample chamber was confirmed using ultraviolet–visible absorption spectroscopy and mass spectrometry, respectively. Enhanced selectivity toward THC over more volatile breath components such as CO2, water, ethanol, methanol, and acetone was achieved by delaying the sensor reading to allow for the desorption of these compounds from the chemiresistor surface. Additionally, machine learning algorithms were utilized to improve the selective detection of THC with better accuracy at increasing quantities of THC delivered to the chemiresistor.
PDF | DOI: 10.3390/s19153303
This study aims to characterize traumatic spinal cord injury (TSCI) neurophysiologically using an intramuscular fine-wire electromyography (EMG) electrode pair. EMG data were collected from an agonist-antagonist pair of tail muscles of Macaca fasicularis, pre- and post-lesion, and for a treatment and control group. The EMG signals were decomposed into multi-resolution subsets using wavelet transforms (WT), then the relative power (RP) was calculated for each individual reconstructed EMG sub-band. Linear mixed models were developed to test three hypotheses: (i) asymmetrical volitional activity of left and right side tail muscles (ii) the effect of the experimental TSCI on the frequency content of the EMG signal, (iii) and the effect of an experimental treatment. The results from the electrode pair data suggested that there is asymmetry in the EMG response of the left and right side muscles (p-value < 0.001). This is consistent with the construct of limb dominance. The results also suggest that the lesion resulted in clear changes in the EMG frequency distribution in the post-lesion period with a significant increment in the low-frequency sub-bands (D4, D6, and A6) of the left and right side, also a significant reduction in the high-frequency sub-bands (D1 and D2) of the right side (p-value < 0.001). The preliminary results suggest that using the RP of the EMG data, the fine-wire intramuscular EMG electrode pair are a suitable method of monitoring and measuring treatment effects of experimental treatments for spinal cord injury (SCI).
PDF | DOI: 10.1098/rsos.181982
Hyoid bone movement is an important physiological event during swallowing that contributes to normal swallowing function. In order to determine the adequate hyoid bone movement, clinicians conduct an X-ray videofluoroscopic swallowing study, which even though it is the gold-standard technique, has limitations such as radiation exposure and cost. Here, we demonstrated the ability to track the hyoid bone movement using a non-invasive accelerometry sensor attached to the surface of the human neck. Specifically, deep neural networks were used to mathematically describe the relationship between hyoid bone movement and sensor signals. Training and validation of the system were conducted on a dataset of 400 swallows from 114 patients. Our experiments indicated the computer-aided hyoid bone movement prediction has a promising performance when compared with human experts’ judgements, revealing that the universal pattern of the hyoid bone movement is acquirable by the highly nonlinear algorithm. Such a sensor-supported strategy offers an alternative and widely available method for online hyoid bone movement tracking without any radiation side-effects and provides a pronounced and flexible approach for identifying dysphagia and other swallowing disorders.
PDF | DOI: 10.1088/1741-2552/ab0b7f
Objective. We aim at developing a hybrid brain–computer interface that utilizes electroencephalography (EEG) and functional transcranial Doppler (fTCD). In this hybrid BCI, EEG and fTCD are used simultaneously to measure electrical brain activity and cerebral blood velocity respectively in response to flickering mental rotation (MR) and word generation (WG) tasks. In this paper, we improve both the accuracy and information transfer rate (ITR) of this novel hybrid brain computer interface (BCI) we designed in our previous work. Approach. To achieve such aim, we extended our feature extraction approach through using template matching and multi-scale analysis to extract EEG and fTCD features, respectively. In particular, template matching was used to analyze EEG data whereas 5-level wavelet decomposition was applied to fTCD data. Significant EEG and fTCD features were selected using Wilcoxon signed rank test. Support vector machines classifier (SVM) was used to project EEG and fTCD selected features of each trial into scalar SVM scores. Moreover, instead of concatenating EEG and fTCD feature vectors corresponding to each trial, we proposed a Bayesian fusion approach of EEG and fTCD evidences. Main results. Average accuracy and average ITR of 98.11% and 21.29 bits min−1 were achieved for WG versus MR classification while MR versus baseline yielded 86.27% average accuracy and 8.95 bit min−1 average ITR. In addition, average accuracy of 85.29% and average ITR of 8.34 bits min−1 were obtained for WG versus baseline. Significance. The proposed analysis techniques significantly improved the hybrid BCI performance. Specifically, for MR/WG versus baseline problems, we achieved twice of the ITRs obtained in our previous study. Moreover, the ITR of WG versus MR problem is 4-times the ITR we obtained before for the same problem. The current analysis methods boosted the performance of our EEG-fTCD BCI such that it outperformed the existing EEG-fNIRS BCIs in comparison.
PDF | DOI: 10.1016/j.compbiomed.2019.02.017
The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.
PDF | DOI: 10.1016/j.jneumeth.2019.03.018
Background: Recently, hybrid brain-computer interfaces (BCIs) combining more than one modality have been investigated with the aim of boosting the performance of the existing single-modal BCIs in terms of accuracy and information transfer rate (ITR). Previously, we introduced a novel hybrid BCI in which EEG and fTCD modalities are used simultaneously to measure electrical brain activity and cerebral blood velocity during motor imagery (MI) tasks. New method: In this paper, we used multi-scale analysis and common spatial pattern algorithm to extract EEG and fTCD features. Moreover, we proposed probabilistic fusion of EEG and fTCD evidences instead of concatenating EEG and fTCD feature vectors corresponding to each trial. A Bayesian approach was proposed to fuse EEG and fTCD evidences under 3 different assumptions. Results: Experimental results showed that 93.85%, 93.71%, and 100% average accuracies and 19.89, 26.55, and 40.83 bits/min average ITRs were achieved for right MI vs baseline, left MI versus baseline, and right MI versus left MI respectively. Comparison with existing methods: These performance measures outperformed the results we obtained before in our preliminary study in which average accuracies of 88.33%, 89.48%, and 82.38% and average ITRs of 4.17, 5.45, and 10.57 bits/min were achieved for right MI versus baseline, left MI versus baseline, and right MI versus left MI respectively. Moreover, in terms of both accuracy and speed, the EEG- fTCD BCI with the proposed analysis techniques outperformed all EEG- fNIRS studies in comparison. Conclusions: The proposed system is a more accurate and faster alternative to EEG-fNIRS systems.
PDF | DOI: 10.1155/2019/3208569
Analysis of vertex-varying spectral content of signals on graphs challenges the assumption of vertex invariance and requires the introduction of vertex-frequency representations as a new tool for graph signal analysis. Local smoothness, an important parameter of vertex-varying graph signals, is introduced and defined in this paper. Basic properties of this parameter are given. By using the local smoothness, an ideal vertex-frequency distribution is introduced. The local smoothness estimation is performed based on several forms of the vertex-frequency distributions, including the graph spectrogram, the graph Rihaczek distribution, and a vertex-frequency distribution with reduced interferences. The presented theory is illustrated through numerical examples.
PDF | DOI: 10.1007/s00034-018-0909-2
Sparse signals are characterized by a few nonzero coefficients in one of their transformation domains. This was the main premise in designing signal compression algorithms. Compressive sensing as a new approach employs the sparsity property as a precondition for signal recovery. Sparse signals can be fully reconstructed from a reduced set of available measurements. The description and basic definitions of sparse signals, along with the conditions for their reconstruction, are discussed in the first part of this paper. The numerous algorithms developed for the sparse signals reconstruction are divided into three classes. The first one is based on the principle of matching components. Analysis of noise and nonsparsity influence on reconstruction performance is provided. The second class of reconstruction algorithms is based on the constrained convex form of problem formulation where linear programming and regression methods can be used to find a solution. The third class of recovery algorithms is based on the Bayesian approach. Applications of the considered approaches are demonstrated through various illustrative and signal processing examples, using common transformation and observation matrices. With pseudocodes of the presented algorithms and compressive sensing principles illustrated on simple signal processing examples, this tutorial provides an inductive way through this complex field to researchers and practitioners starting from the basics of sparse signal processing up to the most recent and up-to-date methods and signal processing applications.
PDF | DOI: 10.1016/j.apmr.2018.05.038
Objective: To examine whether there were any associations between high-resolution cervical auscultation (HRCA) acoustic signals recorded by a contact microphone and swallowing kinematic events during pharyngeal swallow as assessed by a videofluoroscopic (VF) examination. Design: Prospective pilot study. Setting: University teaching hospital, university research laboratories. Participants: Patients (N=35) with stroke who have suspected dysphagia (26 men + 9 women; age = 65.8±11.2). Methods: VF recordings of 100 liquid swallows from 35 stroke patients were analyzed, and a variety of HRCA signal features to characterize each swallow were calculated. Main Outcome Measures: Percent of signal feature maxima (peak) occurring within 0.1 seconds of swallow kinematic event identified from VF recording. Results: Maxima of HRCA signal features, such as standard deviation, skewness, kurtosis, centroid frequency, bandwidth, and wave entropy, were associated with hyoid elevation, laryngeal vestibule closure, and upper esophageal sphincter opening, and the contact of the base of the tongue and posterior pharyngeal wall. Conclusions: Although the kinematic source of HRCA acoustic signals has yet to be fully elucidated, these results indicate a strong relationship between these HRCA signals and several swallow kinematic events. There is a potential for HRCA to be developed for diagnostic and rehabilitative clinical management of dysphagia.
PDF | DOI: 10.1016/j.jneumeth.2018.11.017
Background: Hybrid brain computer interfaces (BCIs) combining multiple brain imaging modalities have been proposed recently to boost the performance of single modality BCIs. New method: In this paper, we propose a novel motor imagery (MI) hybrid BCI that uses electrical brain activity recorded using Electroencephalography (EEG) as well as cerebral blood flow velocity measured using functional transcranial Doppler ultrasound (fTCD). Features derived from the power spectrum for both EEG and fTCD signals were calculated. Mutual information and linear support vector machines (SVM) were employed for feature selection and classification. Results: Using the EEG-fTCD combination, average accuracies of 88.33%, 89.48%, and 82.38% were achieved for right arm MI versus baseline, left arm MI versus baseline, and right arm MI versus left arm MI respectively. Compared to performance measures obtained using EEG only, the hybrid system provided significant improvement in terms of accuracy by 4.48%, 5.36%, and 4.76% respectively. In addition, average transmission rates of 4.17, 5.45, and 10.57 bits/min were achieved for right arm MI versus baseline, left arm MI versus baseline, and right arm MI versus left arm MI respectively. Comparison with existing methods: Compared to EEG-fNIRS hybrid BCIs in literature, we achieved similar or higher accuracies with shorter task duration. Conclusions: The proposed hybrid system is a promising candidate for real-time BCI applications.
PDF | DOI: 10.1021/acsami.8b15785
Carbon nanotube-based field-effect transistors (NTFETs) are ideal sensor devices as they provide rich information regarding carbon nanotube interactions with target analytes and have potential for miniaturization in diverse applications in medical, safety, environmental, and energy sectors. Herein, we investigate chemical detection with cross-sensitive NTFETs sensor arrays comprised of metal nanoparticle-decorated single-walled carbon nanotubes (SWCNTs). By combining analysis of NTFET device characteristics with supervised machine-learning algorithms, we have successfully discriminated among five selected purine compounds, adenine, guanine, xanthine, uric acid, and caffeine. Interactions of purine compounds with metal nanoparticle-decorated SWCNTs were corroborated by density functional theory calculations. Furthermore, by testing a variety of prepared as well as commercial solutions with and without caffeine, our approach accurately discerns the presence of caffeine in 95% of the samples with 48 features using a linear discriminant analysis and in 93.4% of the samples with only 11 features when using a support vector machine analysis. We also performed recursive feature elimination and identified three NTFET parameters, transconductance, threshold voltage, and minimum conductance, as the most crucial features to analyte prediction accuracy.
PDF | DOI: 10.1109/MSP.2018.2875863
Swallowing is a sensorimotor activity by which food, liquids, and saliva pass from the oral cavity to the stomach. It is considered one of the most complex sensorimotor functions because of the high level of coordination needed to accomplish the swallowing task over a very short period of 1-2s and the multiple subsystems it involves. Dysphagia (i.e., swallowing difficulties) refers to any swallowing disorder and is commonly caused by a variety of neurological conditions (e.g., stroke, cerebral palsy, Parkinson disease), head and neck cancer and its treatment, genetic syndromes, and iatrogenic conditions or trauma. The signs and symptoms of dysphagia range from anterior loss of food while eating, difficulty chewing, and subjective difficulty swallowing food or liquids to choking or coughing before, during, or after eating because of impaired clearance of swallowed material from the throat into the digestive system. When not effectively treated, dysphagia can cause malnutrition, dehydration, immune system failure, psychosocial degradation, and generally decreased quality of life.
PDF | DOI: 10.1109/JTEHM.2018.2881468
Millions of people across the globe suffer from swallowing difficulties, known as dysphagia, which can lead to malnutrition, pneumonia, and even death. Swallowing cervical auscultation, which has been suggested as a noninvasive screening method for dysphagia, has not been associated yet with any physical events. In this paper, we have compared the hyoid bone displacement extracted from the videofluoroscopy images of 31 swallows to the signal features extracted from the cervical auscultation recordings captured with a tri-axial accelerometer and a microphone. First, the vertical displacement of the anterior part of the hyoid bone is related to the entropy rate of the superior–inferior swallowing vibrations and to the kurtosis of the swallowing sounds. Second, the vertical displacement of the posterior part of the hyoid bone is related to the bandwidth of the medial–lateral swallowing vibrations. Third, the horizontal displacements of the posterior and anterior parts of the hyoid bone are related to the spectral centroid of the superior–inferior swallowing vibrations and to the peak frequency of the medial–lateral swallowing vibrations, respectively. At last, the airway protection scores and the command characteristics were associated with the vertical and horizontal displacements, respectively, of the posterior part of the hyoid bone. Additional associations between the patients’ characteristics and auscultations’ signals were also observed. The hyoid bone maximal displacement is a cause of swallowing vibrations and sounds. High-resolution cervical auscultation may offer a noninvasive alternative for dysphagia screening and additional diagnostic information.
PDF | DOI: 10.1109/LSENS.2018.2879466
Humans can transfer knowledge previously acquired from a specific task to new and unknown ones. Recently, transfer learning (TL) has been extensively used in brain–computer interface (BCI) research to reduce the training/calibration requirements. BCI systems have been designed to provide alternative communication or control access through computers to individuals with limited speech and physical abilities (LSPA). These systems generally require a calibration session in order to train the BCI before each usage. Such a calibration session may be burdensome for the individuals with LSPA. In this article, we introduce a multimodal hybrid BCI based on electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) and present a TL approach to reduce the calibration requirements. In the hybrid BCI, EEG, and fTCD are used simultaneously to measure the electrical brain activity and cerebral blood velocity, respectively, in response to motor imagery (MI) tasks. Using the data we collected from ten healthy individuals, we perform dimensionality reduction utilizing regularized discriminant analysis (RDA). Using the scores from RDA, we learn class conditional probabilistic distributions for each individual. We use these class conditional distributions to perform TL across different participants. More specifically, in order to reduce the calibration requirements for each individual, we choose the recorded data from other individuals to augment the training data for that specific individual. We choose the data for augmentation based on the probabilistic similarities between the class conditional distributions. For the final classification, we use the RDA scores after TL as features input to three different classifiers: quadratic discriminant analysis (QDA), linear discriminant analysis (LDA), and support vector machines (SVMs). Using our experimental data, we show that TL decreases the calibration requirements up to 87.5 percent. Also by comparing SVM, LDA, and QDA, we observe that the SVM provides the best classification performance.
PDF | DOI: 10.1016/j.softx.2017.08.006
Compressive sensing is a computational framework for acquisition and processing of sparse signals at sampling rates below the rates mandated by the Nyquist sampling theorem. In this paper, we present seven MATLAB functions for compressive sensing based time–frequency processing of sparse nonstationary signals. These functions are developed to reproduce figures in our companion review paper.
PDF | DOI: 10.1109/TBME.2018.2793763
Objective: Up to 10% of free flap cases are compromised, and without prompt intervention, amputation and even death can occur. Hourly monitoring improves salvage rates, but the gold standard for monitoring requires experienced personnel to operate and suffers from high false-positive rates as high as 31% that result in costly and unnecessary surgeries. In this paper, we investigate free flap patency monitoring using automatic hardware-only classification systems that eliminate the need for experienced personnel. The expected flow ranges of the antegrade and retrograde veins for breast reconstruction are studied using a syringe pump to create the laminar flow seen in veins. Methods: Feature data extracted from the Doppler blood flow signals are analyzed for sensitivity, specificity, and false-positive rates. Hardware is built to perform the classification automatically in real-time and output a decision at the end of the observation period. Results: Experimental results using the hardware-only classifier for a 50 ms window size show high sensitivity (96.75%), specificity (90.20%), and low false-positive rate (9.803%). The experimental and theoretical classification results show close agreement. Conclusion: This work indicates that automatic hardware-only classifiers can eliminate the need for experienced personnel to monitor free flap patency. Significance: The hardware-only classification is amenable to a monolithic implementation and future studies should study a totally implantable wirelessly-powered blood flow classifier. The high classifier performance in a short window period indicates that duty-cycled powering can be used to extend the safe operational depth of an implant. This is particularly relevant for the difficult buried free flap applications.
PDF | DOI: 10.1088/1741-2552/aad46f
Objective. In this paper, we introduce a novel hybrid brain–computer interface (BCI) system that measures electrical brain activity as well as cerebral blood velocity using electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD) respectively in response to flickering mental rotation (MR) and flickering word generation (WG) cognitive tasks as well as a fixation cross that represents the baseline. This work extends our previous approach, in which we showed that motor imagery induces simultaneous changes in EEG and fTCD to enable task discrimination; and hence, provides a design approach for a hybrid BCI. Here, we show that instead of using motor imagery, the proposed visual stimulation technique enables the design of an EEG-fTCD based BCI with higher accuracy. Approach. Features based on the power spectrum of EEG and fTCD signals were calculated. Mutual information and support vector machines were used for feature selection and classification purposes. Main results. EEG-fTCD combination outperformed EEG by 4.05% accuracy for MR versus baseline problem and by 5.81% accuracy for WG versus baseline problem. An average accuracy of 92.38% was achieved for MR versus WG problem using the hybrid combination. Average transmission rates of 4.39, 3.92, and 5.60 bits min−1 were obtained for MR versus baseline, WG versus baseline, and MR versus WG problems respectively. Significance. In terms of accuracy, the current visual presentation outperforms the motor imagery visual presentation we designed before for the EEG-fTCD system by 10% accuracy for task versus task problem. Moreover, the proposed system outperforms the state of the art hybrid EEG-fNIRS BCIs in terms of accuracy and/or information transfer rate. Even though there are still limitations of the proposed system, such promising results show that the proposed hybrid system is a feasible candidate for real-time BCIs.
PDF | DOI: 10.1109/JIOT.2018.2849014
The Internet of Medical Things (IoMT) designates the interconnection of communication-enabled medical-grade devices and their integration to wider-scale health networks in order to improve patients' health. However, because of the critical nature of health-related systems, the IoMT still faces numerous challenges, more particularly in terms of reliability, safety, and security. In this paper, we present a comprehensive literature review of recent contributions focused on improving the IoMT through the use of formal methodologies provided by the cyber-physical systems community. We describe the practical application of the democratization of medical devices for both patients and health-care providers. We also identify unexplored research directions and potential trends to solve uncharted research problems.
PDF | DOI: 10.1186/s12938-018-0555-8
Background: There is considerable evidence that a person’s gait is affected by cognitive load. Research in this field has implications for understanding the relationship between motor control and neurological conditions in aging and clinical populations. Accordingly, this pilot study evaluates the cognitive load based on gait accelerometry measurements of the walking patterns of ten healthy individuals (18–35 years old). Methods: Data points were collected using six triaxial accelerometer sensors and treadmill pressure reports. Stride and window extraction methods were used to process these data points and separate into statistical features. A binary classification was created by using logistic regression, support vector machine, random forest, and learning vector quantization to classify cognitive load vs. no cognitive load. Results: Within and between subjects, a cognitive load was predicted with accuracy values ranged of 0.93–1 by all four models. Various feature selection methods demonstrated that only 2–20 variables could be used to achieve similar levels of accuracies. Conclusion: Coupling sensors with machine learning algorithms to detect the most minute changes in gait patterns, most of which are too subtle to identify with the human eye, may have a remarkable impact on the potential to detect potential neuromotor illnesses and fall risks. In doing so, we can open a new window to human health and safety prevention.
PDF | DOI: 10.1093/jamia/ocy055
This paper presents the development and real-time testing of an automated expert diagnostic telehealth system for the diagnosis of 2 respiratory diseases, asthma and Chronic Obstructive Pulmonary Disease (COPD). The system utilizes Android, Java, MATLAB, and PHP technologies and consists of a spirometer, mobile application, and expert diagnostic system. To evaluate the effectiveness of the system, a prospective study was carried out in 3 remote primary healthcare institutions, and one hospital in Bosnia and Herzegovina healthcare system. During 6 months, 780 patients were assessed and diagnosed with an accuracy of 97.32%. The presented approach is simple to use and offers specialized consultations for patients in remote, rural, and isolated communities, as well as old and less physically mobile patients. While improving the quality of care delivered to patients, it was also found to be very beneficial in terms of healthcare.
PDF | DOI: 10.1111/jgs.15341
OBJECTIVES: To compare the trajectories of motor and cognitive decline in older adults who progress to dementia with the trajectories of those who do not. To evaluate the added value of measuring motor and cognitive decline longitudinally versus cross-sectionally for predicting dementia. DESIGN: Prospective cohort study with 5 years of follow-up. SETTING: Clinic based at a university hospital in London, Ontario, Canada. PARTICIPANTS: Community-dwelling participants aged 65 and older free of dementia at baseline (N=154). MEASUREMENTS: We evaluated trajectories in participants' motor performance using gait velocity and cognitive performance using the MoCA test twice a year for 5 years. We ascertained incident dementia risk using Cox regression models and attributable risk analyses. Analyses were adjusted using a time-dependent covariate. RESULTS: Overall, 14.3% progressed to dementia. The risk of dementia was almost 7 times as great for those whose gait velocity declined (hazard ratio (HR)=6.89, 95% confidence interval (CI)=2.18-21.75, p=.001), more than 3 times as great for those with cognitive decline (HR=3.61, 95% CI=1.28-10.13, p=.01), and almost 8 times as great in those with combined gait velocity and cognitive decline (HR=7.83, 95% CI=2.10-29.24, p=.002), with an attributable risk of 105 per 1,000 person years. Slow gait at baseline alone failed to predict dementia (HR=1.16, 95% CI=0.39-3.46, p=.79). CONCLUSION: Motor decline, assessed according to serial measures of gait velocity, had a higher attributable risk for incident dementia than did cognitive decline. A decline over time of both gait velocity and cognition had the highest attributable risk. A single time-point assessment was not sufficient to detect individuals at high risk of dementia.
PDF | DOI: 10.1109/LSP.2018.2860250
Vertex-frequency analysis of graph signals is a challenging topic for research and applications. Counterparts of the short-time Fourier transform, the wavelet transform, and the Rihaczek distribution have recently been introduced to the graph-signal analysis. In this letter, we have extended the energy distributions to a general reduced interference distributions class. It can improve the vertex-frequency representation of a graph signal while preserving the marginal properties. This class is related to the spectrogram of graph signals as well. Efficiency of the proposed representations is illustrated in examples.
PDF | DOI: 10.1038/s41598-018-30182-6
The displacement of the hyoid bone is one of the key components evaluated in the swallow study, as its motion during swallowing is related to overall swallowing integrity. In daily research settings, experts visually detect the hyoid bone in the video frames and manually plot hyoid bone position frame by frame. This study aims to develop an automatic method to localize the location of the hyoid bone in the video sequence. To automatically detect the location of the hyoid bone in a frame, we proposed a single shot multibox detector, a deep convolutional neural network, which is employed to detect and classify the location of the hyoid bone. We also evaluated the performance of two other state-of-art detection methods for comparison. The experimental results clearly showed that the single shot multibox detector can detect the hyoid bone with an average precision of 89.14% and outperform other auto-detection algorithms. We conclude that this automatic hyoid bone tracking system is accurate enough to be widely applied as a pre-processing step for image processing in dysphagia research, as well as a promising development that may be useful in the diagnosis of dysphagia.
PDF | DOI: 10.1109/TMTT.2018.2811497
Wirelessly powered implantable medical devices require efficient power transfer through biological tissue within safety constraints on energy absorption, often in the presence of environmental variability. Accurate modeling of the tissue medium is essential to evaluate the performance and sensitivity of transcutaneous powering systems. Here, we investigate loop and dipole antenna topologies in proximity to simulated tissue models and experimental phantoms, with emphasis on representing heterogeneous tissue with functionally equivalent simplified models, and modeling variability in tissue properties for sensitivity analyses. We first present a modified phantom formulation that provides greater control over frequency-dependent properties. We then show that homogeneous phantoms have limited use at representing input impedance and energy absorption at ultrahigh operating frequency by analyzing each antenna topology in proximity to layered or homogeneous tissue across frequency. We compare loop and dipole antenna topologies in terms of specific absorption rate and impedance, and show that frequency-dependent tissue behavior must be considered even at fixed operating frequencies. Finally, we discuss the dual utility of a transmitting antenna as a resonator to detect changes in tissue properties in addition to powering an implanted device.
PDF | DOI: 10.1016/j.dsp.2017.07.016
Compressive sensing is a framework for acquiring sparse signals at sub-Nyquist rates. Once compressively acquired, many signals need to be processed using advanced techniques such as time–frequency representations. Hence, we overview recent advances dealing with time–frequency processing of sparse signals acquired using compressive sensing approaches. The paper is geared towards signal processing practitioners and we emphasize practical aspects of these algorithms. First, we briefly review the idea of compressive sensing. Second, we review two major approaches for compressive sensing in the time–frequency domain. Thirdly, compressive sensing based time–frequency representations are reviewed followed by descriptions of compressive sensing approaches based on the polynomial Fourier transform and the short-time Fourier transform. Lastly, we provide brief conclusions along with several future directions for this field.
PDF | DOI: 10.1186/s12938-018-0501-9
BACKGROUND: To utilize cervical auscultation as a means of screening for risk of dysphagia, we must first determine how the signal differs between healthy subjects and subjects with swallowing disorders. METHODS: In this experiment we gathered swallowing sound and vibration data from 53 (13 with stroke, 40 without) patients referred for imaging evaluation of swallowing function with videofluoroscopy. The analysis was limited to non-aspirating swallows of liquid with either thin (less than 5 cps) or viscous (approx. 300 cps) consistency. After calculating a selection of generalized time, frequency, and time frequency features for each swallow, we compared our data against our findings in a previous experiment that investigated identical features for a different group of 56 healthy subjects. RESULTS: We found that nearly all of our chosen features for both vibrations and sounds showed significant differences between the healthy and disordered swallows despite the absence of aspiration. We also found only negligible differences between dysphagia as a symptom of stroke and dysphagia as a symptom of another condition. CONCLUSION: Non-aspirating swallows from healthy controls and patients with dysphagia have distinct feature patterns. These findings should greatly help the development of the cervical auscultation field and serve as a reference for future investigations into more specialized characterization methods.
PDF | DOI: 10.1109/JBHI.2017.2727218
Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this manuscript, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state of- the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.
PDF | DOI: 10.1016/j.neucom.2017.12.059
Cervical auscultation is a method for assessing swallowing performance. However, its ability to serve as a classification tool for a practical clinical assessment method is not fully understood. In this study, we utilized neural network classification methods in the form of Deep Belief networks in order to classify swallows. We specifically utilized swallows that did not result in clinically significant aspiration and classified them on whether they originated from healthy subjects or unhealthy patients. Dual-axis swallowing vibrations from 1946 discrete swallows were recorded from 55 healthy and 53 unhealthy subjects. The Fourier transforms of both signals were used as inputs to the networks of various sizes. We found that single and multi-layer Deep Belief networks perform nearly identically when analyzing only a single vibration signal. However, multi-layered Deep Belief networks demonstrated approximately a 5–10% greater accuracy and sensitivity when both signals were analyzed concurrently, indicating that higher-order relationships between these vibrations are important for classification and assessment.
PDF | DOI: 10.1109/JSEN.2017.2787498
More than five million adults in U.S. are affected by the peripheral artery disease. Current wireless stent monitors are unsuitable to use in the thigh. In this paper, wireless powering and communication with implanted stents were investigated. Specifically, we investigated the effects of thigh tissue morphology and tissue thickness variations on wireless power gain and electromagnetic safety when using skin-contact touch probe antennas. Thigh simulation models were derived from anthropometric data for the diseased population. Power gain and specific absorption rate were determined for each variation. To corroborate human model simulation results, a power-to-frequency converter was designed, benchmarked, and implanted within ex vivo porcine tissue. The experiments showed the most realistic simulations reported so far that have the best agreement with measured results. This paper indicates that touch probe powered stent systems can safely deliver significant power to an implant. This research enables frequent at-home monitoring to replace costly in-hospital quarterly check-ups.
PDF | DOI: 10.1109/LSP.2017.2764884
A vertex-varying spectral content on graphs challenges the assumption of vertex invariance and requires vertex-frequency representations for an adequate analysis. In this letter, we introduce a class of vertex-frequency energy distributions inspired by traditional time-frequency energy distributions. These newly introduced distributions do not use localization windows. Their efficiency in energy concentration is illustrated through examples.
PDF | DOI: 10.1007/s00429-017-1535-7
It has been shown that swallowing involves certain attentional and cognitive resources which, when disrupted can influence swallowing function with in dysphagic patient. However, there are still open questions regarding the influence of attention and cognitive demands on brain activity during swallowing. In order to understand how brain regions responsible for attention influence brain activity during swallowing, we compared brain organization during no-distraction swallowing and swallowing with distraction. Fifteen healthy male adults participated in the data collection process. Participants performed ten 1 ml, ten 5 ml, and ten 10 ml water swallows under both no-distraction conditions and during distraction while EEG signals were recorded. After standard pre-processing of the EEG signals, brain networks were formed using the time-frequency based synchrony measure. The brain networks formed were then compared between the two sets of conditions. Results showed that there are differences in the Delta, Theta, Alpha, Beta, and Gamma frequency bands between no-distraction swallowing and swallowing with distraction. Differences in the Delta and Theta frequency bands can be attributed to changes in subliminal processes, while changes in the Alpha and Beta frequency bands are directly associated with the various levels of attention and cognitive demands during swallowing process, and changes in the Gamma frequency band are due to changes in motor activity. Furthermore, we showed that variations in bolus volume influenced the swallowing brain networks in the Delta, Theta, Alpha, Beta, and Gamma frequency bands. Changes in the Delta, Theta, and Alpha frequency bands are due to sensory perturbations evoked by the various bolus volumes. Changes in the Beta frequency band are due to reallocation of cognitive demands, while changes in the Gamma frequency band are due to changes in motor activity produced by variations in bolus volume. These findings could potentially lead to the development of better understanding of the nature of dysphagia and various rehabilitation strategies for patients with neurogenic dysphagia who have altered attention or impaired cognitive functions.
This article presents the results of experiments involving Macaca fasicularis monkeys, where the experimental lesions were created with a balloon catheter that was inserted into the epidural space. Prior to creation of the lesion, an EMG recording device is inserted; this facilitates measurement of tail movement and muscle activity before and after TSCI. This model is unique in that the impairment is limited to the tail; the subjects do not experience limb weakness, bladder impairment and/or bowel dysfunction. Four of the 6 subjects received a combination treatment of Thyrotropin Releasing Hormone (TRH), selenium, and vitamin E after the experimental TSCI. The subjects tolerated the implantation of the recording device and did not experience adverse effects due medications administered. The electromyographic data was transformed into a metric of volitional tail moment (“Q”). This metric appears to be valid measure of impairment to a measure of impairment and recovery. The histopatholgical assessment demonstrated widespread axon loss at the site of injury and areas cephald and caudal. There was histopathological evidence of continuing inflammation, with macrophage activation. No treatment effect was recognized, based on the electromyographic data.
PDF | DOI: 10.1109/JSEN.2017.2765239
Free flap surgeries require hourly monitoring to detect vascular compromise. If not caught promptly, the flap can be lost. Monitoring free flaps using the gold standard requires experienced operators to interpret blood flow signatures, which are often difficult to distinguish from background noise. Previously reported hardware-only automatic patency classification showed a high sensitivity, specificity, and a low falsepositive rate, but it was demonstrated using bulky discrete electronics and a syringe pump to generate the expected flow rates. In this paper, we investigate automatic hardware-only patency classification on blood flow data collected from the bilateral femoral veins during flow and occluded states using SPICE simulations in an IBM 130-nm CMOS process with a 1-V supply voltage and a 200-ms window length. Experimental results show a very high sensitivity (99.45%), specificity (99.93%), and very low false-positive rate (0.07275%) at just 8.715 μA. This paper shows that automatic hardware-only patency classification is effective for monitoring patency on real pig blood flow data. The demonstrated classifier's performance makes it suitable for integration as part of a wirelessly-powered biomedical patency monitor.
PDF | DOI: 10.1016/j.jneumeth.2017.10.003
BACKGROUND: Functional transcranial Doppler (fTCD) is an ultrasound based neuroimaging technique used to assess neural activation that occurs during a cognitive task through measuring velocity of cerebral blood flow. NEW METHOD: The objective of this paper is to investigate the feasibility of a 2-class and 3-class real-time BCI based on blood flow velocity in left and right middle cerebral arteries in response to mental rotation and word generation tasks. Statistical features based on a five-level wavelet decomposition were extracted from the fTCD signals. The Wilcoxon test and support vector machines (SVM), with a linear kernel, were employed for feature reduction and classification. RESULTS: The experimental results showed that within approximately 3s of the onset of the cognitive task average accuracies of 80.29%, and 82.35% were obtained for the mental rotation versus resting state and the word generation versus resting state respectively. The mental rotation task versus word generation task achieved an average accuracy of 79.72% within 2.24s from the onset of the cognitive task. Furthermore, an average accuracy of 65.27% was obtained for the 3-class problem within 4.68s. COMPARISON WITH EXISTING METHODS: The results presented here provide significant improvement compared to the relevant fTCD-based systems presented in literature in terms of accuracy and speed. Specifically, the reported speed in this manuscript is at least 12 and 2.5 times faster than any existing binary and 3-class fTCD-based BCIs, respectively. CONCLUSIONS: These results show fTCD as a promising and viable candidate to be used towards developing a real-time BCI.
PDF | DOI: 10.1007/s11517-017-1659-1
Mild-to-moderate ischemia does not result in ST segment elevation on the electrocardiogram (ECG), but rather non-specific changes in the T wave, which are frequently labeled as non-diagnostic for ischemia. Robust methods to quantify such T wave heterogeneity can have immediate clinical applications. We sought to evaluate the effects of spontaneous ischemia on the evolution of spatial T wave changes, based on the eigenvalues of the spatial correlation matrix of the ECG, in patients undergoing nuclear cardiac imaging for evaluating intermittent chest pain. We computed T wave complexity (TWC), the ratio of the second to the first eigenvalue of repolarization, from 5-min baseline and 5-min peak-stress Holter ECG recordings. Our sample included 30 males and 20 females aged 63 ± 11 years. Compared to baseline, significant changes in TWC were only seen in patients with ischemia (n = 10) during stress testing, but not among others. The absolute changes in TWC were significantly larger in the ischemia group compared to others, with a pattern that seemed to depend on the severity or anatomic distribution of ischemia. Our results demonstrate that ischemia-induced changes in T wave morphology can be meaningfully quantified from the surface 12-lead ECG, suggesting an important opportunity for improving diagnostics in patients with chest pain.
PDF | DOI: 10.1007/s40520-016-0693-4
Research on balance and mobility in older adults has been conducted primarily in lab-based settings in individuals who live in the community. Although they are at greater risk of falls, residents of long-term care facilities, specifically residential care communities (RCCs), have been investigated much less frequently. We sought to determine the feasibility of using portable technology-based measures of balance and muscle strength (i.e., an accelerometer and a load cell) that can be used in any RCC facility. Twenty-nine subjects (age 87 ± 6 years) living in RCCs participated. An accelerometer placed on the back of the subjects measured body sway during different standing conditions. Sway in antero-posterior and mediolateral directions was calculated. Lower extremity strength was measured with a portable load cell and the within-visit reliability was determined. Assessments of grip strength, gait speed, frailty, and comorbidity were also examined. A significant increase in postural sway in both the AP and ML directions occurred as the balance conditions became more difficult due to alteration of sensory feedback or reducing the base of support. There was an association between increased sway and increased frailty, more comorbidities and slower gait speed. All strength measurements were highly reliable (ICC = 0.93–0.99). An increase in lower extremity strength was associated with increased grip strength and gait speed. The portable instruments provide inexpensive ways for measuring balance and strength in the understudied RCC population, but additional studies are needed to examine their relationship with functional outcomes.
PDF | DOI: 10.1109/JSEN.2017.2748338
Wireless electromagnetic powering of implantable medical devices is a diverse research area, with goals including replacing percutaneous wires, miniaturizing and extending the lifetime of implanted devices, enabling wireless communication, and biosensing, all while maximizing safety and efficiency of wireless power transfer. Many challenges in wireless transcutaneous powering are associated with tissue as an electromagnetic transmission medium. Tissue is lossy and variable, and safety is a concern due to absorption of electromagnetic energy in high-water-content tissue. The purpose of this overview is to summarize reported variability of tissue properties, particularly in the context of electromagnetic safety, with a focus on models of tissue that can represent variability in the design and evaluation of systems for wireless transcutaneous power transfer.
PDF | DOI: 10.1007/s10916-017-0783-7
To improve the quality of patient treatment by improving the functionality of medical devices in healthcare institutions. To present the results of the safety and performance inspection of patient-relevant output parameters of anesthesia machines and defibrillators defined by legal metrology. This study covered 130 anesthesia machines and 161 defibrillators used in public and private healthcare institutions, during a period of two years. Testing procedures were carried out according to international standards and legal metrology legislative procedures in Bosnia and Herzegovina. The results show that in 13.84% of tested anesthesia machine and 14.91% of defibrillators device performance is not in accordance with requirements and should either have its results be verified, or the device removed from use or scheduled for corrective maintenance. Research emphasizes importance of independent safety and performance inspections, and gives recommendations for the frequency of inspection based on measurements. Results offer implications for adequacy of preventive and corrective maintenance performed in healthcare institutions. Based on collected data, the first digital electronical database of anesthesia machines and defibrillators used in healthcare institutions in Bosnia and Herzegovina is created. This database is a useful tool for tracking each device’s performance over time.
PDF | DOI: 10.1109/JTEHM.2017.2723391
The design of effective transcutaneous systems demands the consideration of inevitable variations in tissue characteristics, which vary across body areas, among individuals, and over time. The purpose of this paper was to design and evaluate several printed antenna topologies for ultrahigh frequency (UHF) transcutaneous power transfer to implantable medical devices, and to investigate the effects of variations in tissue properties on dipole and loop topologies. Here, we show that a loop antenna topology provides the greatest achievable gain with the smallest implanted antenna, while a dipole system provides higher impedance for conjugate matching and the ability to increase gain with a larger external antenna. In comparison to the dipole system, the loop system exhibits greater sensitivity to changes in tissue structure and properties in terms of power gain, but provides higher gain when the separation is on the order of the smaller antenna dimension. The dipole system was shown to provide higher gain than the loop system at greater implant depths for the same implanted antenna area, and was less sensitive to variations in tissue properties and structure in terms of power gain at all investigated implant depths. The results show the potential of easily-fabricated, low-cost printed antenna topologies for UHF transcutaneous power, and the importance of environmental considerations in choosing the antenna topology.
PDF | DOI: 10.1016/j.compbiomed.2017.05.020
Writing is a complex fine and trained motor skill, involving complex biomechanical and cognitive processes. In this paper, we propose the study of writing kinetics using three angles: the pen-tip normal force, the total grip force signal and eventually writing quality assessment. In order to collect writing kinetics data, we designed a sensor collecting these characteristics simultaneously. Ten healthy right-handed adults were recruited and were asked to perform four tasks: first, they were instructed to draw circles at a speed they considered comfortable; they then were instructed to draw circles at a speed they regarded as fast; afterwards, they repeated the comfortable task compelled to follow the rhythm of a metronome; and eventually they performed the fast task under the same timing constraints. Statistical differences between the tasks were computed, and while pen-tip normal force and total grip force signal were not impacted by the changes introduced in each task, writing quality features were affected by both the speed changes and timing constraint changes. This verifies the already-studied speed-accuracy trade-off and suggest the existence of a timing constraints-accuracy trade-off.
PDF | DOI: 10.1001/jamaneurol.2017.0643
Importance: Gait performance is affected by neurodegeneration in aging and has the potential to be used as a clinical marker for progression from mild cognitive impairment (MCI) to dementia. A dual-task gait test evaluating the cognitive-motor interface may predict dementia progression in older adults with MCI. Objective: To determine whether a dual-task gait test is associated with incident dementia in MCI. Design, Setting, and Participants: The Gait and Brain Study is an ongoing prospective cohort study of community-dwelling older adults that enrolled 112 older adults with MCI. Participants were followed up for 6 years, with biannual visits including neurologic, cognitive, and gait assessments. Data were collected from July 2007 to March 2016. Main Outcomes and Measures: Incident all-cause dementia was the main outcome measure, and single- and dual-task gait velocity and dual-task gait costs were the independent variables. A neuropsychological test battery was used to assess cognition. Gait velocity was recorded under single-task and 3 separate dual-task conditions using an electronic walkway. Dual-task gait cost was defined as the percentage change between single- and dual-task gait velocities: ([single-task gait velocity – dual-task gait velocity]/ single-task gait velocity) × 100. Cox proportional hazard models were used to estimate the association between risk of progression to dementia and the independent variables, adjusted for age, sex, education, comorbidities, and cognition. Results: Among 112 study participants with MCI, mean (SD) age was 76.6 (6.9) years, 55 were women (49.1%), and 27 progressed to dementia (24.1%), with an incidence rate of 121 per 1000 person-years. Slow single-task gait velocity (less than 0.8 m/second) was not associated with progression to dementia (hazard ratio [HR], 3.41; 95% CI, 0.99-11.71; P = .05)while high dual-task gait cost while counting backward (HR, 3.79; 95% CI, 1.57-9.15; P=.003) and naming animals (HR, 2.41; 95% CI, 1.04-5.59; P = .04) were associated with dementia progression (incidence rate, 155 per 1000 person-years). The models remained robust after adjusting by baseline cognition except for dual-task gait cost when dichotomized. Conclusions and Relevance: Dual-task gait is associated with progression to dementia in patients with MCI. Dual-task gait testing is easy to administer and may be used by clinicians to decide further biomarker testing, preventive strategies, and follow-up planning in patients with MCI.
PDF | DOI: 10.1109/MSP.2017.2696572
Currently, brain and social networks are examples of new data types that are massively acquired and disseminated. These networks typically consist of vertices (nodes) and edges (connections between nodes). Usually, information is conveyed through the strength of connection among nodes, but in recent years, it has been discovered that valuable information may also be conveyed in signals that occur on each vertex. However, traditional signal processing often does not offer reliable tools and algorithms to analyze such new data types. This is especially true for cases where networks (e.g., the strength of connections), or signals on vertices, have properties that change over the network. This lecture note presents a new method to analyze changes in signals on graphs. This method, called the vertex-frequency analysis, relies on Laplacian matrices to establish connections between vertex changes and spectral content. Specifically, this lecture note aims to connect concepts from frequency and time-frequency analyses to the spectral analysis of graph signals. Graph signal processing is a major research area, however, we still lack understanding of how to relate graph signal processing concepts to concepts from traditional signal processing.
PDF | DOI: 10.1016/j.cmpb.2017.03.009
The cervical auscultation refers to the observation and analysis of sounds or vibrations captured during swallowing using either a stethoscope or acoustic/vibratory detectors. Microphones and accelerometers have recently become two common sensors used in modern cervical auscultation methods. There are open questions about whether swallowing signals recorded by these two sensors provide unique or complementary information about swallowing function; or whether they present interchangeable information. This study aims to compare of swallowing signals recorded by a microphone and a tri-axial accelerometer from 72 patients (mean age 63.94 ± 12.58 years, 42 male, 30 female), who had videofluoroscopic examination. The participants swallowed one or more boluses of thickened liquids of different consistencies, including thin liquids, nectar-thick liquids, and pudding. A comfortable self-selected volume from a cup or a controlled volume by the examiner from a 5 ml spoon was given to the participants. A broad feature set was extracted in time, information-theoretic, and frequency domains from each of 881 swallows presented in this study. The swallowing sounds exhibited significantly higher frequency content and kurtosis values than the swallowing vibrations. In addition, the Lempel–Ziv complexity was lower for swallowing sounds than those for swallowing vibrations. To conclude, information provided by microphones and accelerometers about swallowing function are unique and these two transducers are not interchangeable. Consequently, the selection of transducer would be a vital step in future studies.
PDF | DOI: 10.1109/TNSRE.2016.2577882
Swallowing accelerometry is a non-invasive approach currently under consideration as an instrumental screening test for swallowing difficulties, with most current studies focusing on the swallowing vibrations in the anterior-posterior (AP) and superior-interior (S-I) directions. However, the displacement of the hyolaryngeal structure during the act of swallowing in patients with dysphagia involves declination of the mediallateral (M-L), which suggests that the swallowing vibrations in the M-L direction have the ability to reveal additional details about the swallowing function. With this motivation, we performed a broad comparison of the swallowing vibrations in all three anatomical directions. Tri-axial swallowing accelerometry signals were concurrently collected from 72 dysphagic patients undergoing videofluoroscopic evaluation of swallowing (mean age: 63.94 12.58 years period). Participants swallowed one or more thickened liquids with different consistencies including thin-thick liquids, nectar-thick liquids, and pudding-thick liquids with either a comfortable self-selected volume from a cup or a controlled volume by the examiner from a 5ml spoon. Swallows were grouped based on the viscosity of swallows and the participant’s stroke history. Then, a comprehensive set of features was extracted in multiple signal domains from 881 swallows. The results highlighted inter-axis dissimilarities among tri-axial swallowing vibrations including the extent of variability in the amplitude of signals, the degree of predictability of signals, and the extent of disordered behavior of signals in time-frequency domain. First, the upward movement of the hyolaryngeal structure, representing the S-I signals, were actually more variable in amplitude and showed less predictable behavior than the sideways and forward movements, representing the A-P and M-L signals, during swallowing. Second, the S-I signals, which represent the upward movement of the hyolaryngeal structure, behaved more disordered in the time-frequency domain than the sideways movement, M-L signals, in all groups of study except for the pudding swallows in the stroke group. Third, considering the viscosity and the participant’s pathology, thin liquid swallows in the non-stroke group presented the most directional differences among all groups of study. In summary, despite some directional dissimilarities, M-L axis accelerometry characteristics are similar to those of the two other axes. This indicates that M-L axis characteristics, which cannot be observed in videofluoroscopic images, can be adequately derived from the A-P and S-I axes.
PDF | DOI: 10.1016/j.ultrasmedbio.2016.11.005
Totally implantable wireless ultrasonic blood flowmeters provide direct-access chronic vessel monitoring in hard-to-reach places without using wired bedside monitors or imaging equipment. Although wireless implantable Doppler devices are accurate for most applications, device size and implant lifetime remain vastly underdeveloped. We review past and current approaches to miniaturization and implant lifetime extension for wireless implantable Doppler devices and propose approaches to reduce device size and maximize implant lifetime for the next generation of devices. Additionally, we review current and past approaches to accurate blood flow measurements. This review points toward relying on increased levels of monolithic customization and integration to reduce size. Meanwhile, recommendations to maximize implant lifetime should include alternative sources of power, such as transcutaneous wireless power, that stand to extend lifetime indefinitely. Coupling together the results will pave the way for ultra-miniaturized totally implantable wireless blood flow monitors for truly chronic implantation.
PDF | DOI: 10.1016/j.neuroscience.2016.11.047
Patients with dysphagia can have higher risks of aspiration after repetitive swallowing activity due to the “fatigue effect”. However, it is still unknown how consecutive swallows affect brain activity. Therefore, we sought to investigate differences in swallowing brain networks formed during consecutive swallows using a signal processing on graph approach. Data were collected from 55 healthy people using electroencephalography (EEG) signals. Participants performed dry swallows (i.e., saliva swallows) and wet swallows (i.e., water, nectar-thick, and honey thick swallows). After standard pre-processing of the EEG time series, brain networks were formed using the time–frequency-based synchrony measure, while signals on graphs were formed as a line graph of the brain networks. For calculating the vertex frequency information from the signals on graphs, the proposed algorithm was based on the optimized window size for calculating the windowed graph Fourier transform and the graph S-transform. The proposed algorithms were tested using synthetic signals and showed improved energy concentration in comparison to the original algorithm. When applied to EEG swallowing data, the optimized windowed graph Fourier transform and the optimized graph S-transform showed that differences exist in brain activity between consecutive swallows. In addition, the results showed higher differences between consecutive swallows for thicker liquids.
PDF | DOI: 10.1016/j.bbr.2016.11.021
Previous studies have shown the functional neural circuitry differences before and after an explicitly learned motor sequence task, but have not assessed these changes during the process of motor skill learning. Functional magnetic resonance imaging activity was measured while participants (n = 13) were asked to tap their fingers to visually presented sequences in blocks that were either the same sequence repeated (learning block) or random sequences (control block). Motor learning was associated with a decrease in brain activity during learning compared to control. Lower brain activation was noted in the posterior parietal association area and bilateral thalamus during the later periods of learning (not during the control). Compared to the control condition, we found the task-related motor learning was associated with decreased connectivity between the putamen and left inferior frontal gyrus and left middle cingulate brain regions. Motor learning was associated with changes in network activity, spatial extent, and connectivity.
PDF | DOI: 10.1016/j.sigpro.2016.09.008
The windowed Fourier transform (short time Fourier transform) and the S-transform are widely used signal processing tools for extracting frequency information from non-stationary signals. Previously, the windowed Fourier transform had been adopted for signals on graphs and has been shown to be very useful for extracting vertex-frequency information from graphs. However, high computational complexity makes these algorithms impractical. We sought to develop a fast windowed graph Fourier transform and a fast graph S-transform requiring significantly shorter computation time. The proposed schemes have been tested with synthetic test graph signals and real graph signals derived from electroencephalography recordings made during swallowing. The results showed that the proposed schemes provide significantly lower computation time in comparison with the standard windowed graph Fourier transform and the fast graph S-transform. Also, the results showed that noise has no effect on the results of the algorithm for the fast windowed graph Fourier transform or on the graph S-transform. Finally, we showed that graphs can be reconstructed from the vertex-frequency representations obtained with the proposed algorithms.
PDF | DOI: 10.1016/j.compbiomed.2016.11.009
Human gait is a complex interaction of many nonlinear systems and stride intervals exhibiting self-similarity over long time scales that can be modeled as a fractal process. The scaling exponent represents the fractal degree and can be interpreted as a “biomarker” of relative diseases. The previous study showed that the average wavelet method provides the most accurate results to estimate this scaling exponent when applied to stride interval time series. The purpose of this paper is to determine the most suitable mother wavelet for the average wavelet method. This paper presents a comparative numerical analysis of 16 mother wavelets using simulated and real fractal signals. Simulated fractal signals were generated under varying signal lengths and scaling exponents that indicate a range of physiologically conceivable fractal signals. The five candidates were chosen due to their good performance on the mean square error test for both short and long signals. Next, we comparatively analyzed these five mother wavelets for physiologically relevant stride time series lengths. Our analysis showed that the symlet 2 mother wavelet provides a low mean square error and low variance for long time intervals and relatively low errors for short signal lengths. It can be considered as the most suitable mother function without the burden of considering the signal length.
PDF | DOI: 10.1108/COMPEL-05-2016-0198
Purpose: The purpose of this paper is to propose a new algorithm for detection of chaos in oscillatory circuits. The algorithm is based on the wavelet transform. Design/methodology/approach: The proposed detection is developed by using a specific measure obtained by averaging wavelet coefficients. This measure exhibits various values for chaotic and periodic states. Findings: The proposed algorithm is applied to signals from autonomous systems such as the Chua’s oscillatory circuit, the Lorenz chaotic system and non-autonomous systems such as the Duffing oscillator. In addition, the detection is applied to sequences obtained from the logistic map. The results are compared to those obtained with a detrended fluctuation analysis and a time-frequency signal analysis based on detectors of chaotic states. Originality/value: In this paper, a new algorithm is proposed for the detection of chaos from a single time series. The proposed technique is robust to the noise influence, having smaller calculation complexity with respect to the state-of-the-art techniques. It is suitable for real-time detection with delay that is about half of the window width.
PDF | DOI: 10.1016/j.cmpb.2016.09.016
Background and objective: Smoking is the largest preventable cause of death and diseases in the developed world, and advances in modern electronics and machine learning can help us deliver real-time intervention to smokers in novel ways. In this paper, we examine different machine learning approaches to use situational features associated with having or not having urges to smoke during a quit attempt in order to accurately classify high-urge states. Methods: To test our machine learning approaches, specifically, Bayes, discriminant analysis and decision tree learning methods, we used a dataset collected from over 300 participants who had initiated a quit attempt. The three classification approaches are evaluated observing sensitivity, specificity, accuracy and precision. Results: The outcome of the analysis showed that algorithms based on feature selection make it possible to obtain high classification rates with only a few features selected from the entire dataset. The classification tree method outperformed the naive Bayes and discriminant analysis methods, with an accuracy of the classifications up to 86%. These numbers suggest that machine learning may be a suitable approach to deal with smoking cessation matters, and to predict smoking urges, outlining a potential use for mobile health applications. Conclusions: In conclusion, machine learning classifiers can help identify smoking situations, and the search for the best features and classifier parameters significantly improves the algorithms' performance. In addition, this study also supports the usefulness of new technologies in improving the effect of smoking cessation interventions, the management of time and patients by therapists, and thus the optimization of available health care resources. Future studies should focus on providing more adaptive and personalized support to people who really need it, in a minimum amount of time by developing novel expert systems capable of delivering real-time interventions.
PDF | DOI: 10.1016/j.brainres.2016.09.041
Consuming thicker fluids and swallowing in the chin-tuck position has been shown to be advantageous for some patients with neurogenic dysphagia who aspirate due to various causes. The anatomical changes caused by these therapeutic techniques are well known, but it is unclear whether these changes alter the cerebral processing of swallow-related sensorimotor activity. We sought to investigate the effect of increased fluid viscosity and chin-down posture during swallowing on brain networks. 55 healthy adults performed water, nectar-thick, and honey thick liquid swallows in the neutral and chin-tuck positions while EEG signals were recorded. After pre-processing of the EEG timeseries, the time-frequency based synchrony measure was used for forming the brain networks to investigate whether there were differences among the brain networks between the swallowing of different fluid viscosities and swallowing in different head positions. We also investigated whether swallowing under various conditions exhibit small-world properties. Results showed that fluid viscosity affects the brain network in the Delta, Theta, Alpha, Beta, and Gamma frequency bands and that swallowing in the chin-tuck head position affects brain networks in the Alpha, Beta, and Gamma frequency bands. In addition, we showed that swallowing in all tested conditions exhibited small-world properties. Therefore, fluid viscosity and head positions should be considered in future swallowing EEG investigations.
PDF | DOI: 10.1109/JTEHM.2016.2588504
Current totally implantable wireless blood flow monitors are large and cannot operate alongside nearby monitors. To alleviate the problems with the current monitors, we developed a system to monitor blood flow wirelessly, with a simple and easily interpretable real-time output. To the best of our knowledge, the implanted electronics are the smallest in reported literature, which reduces bio-burden. Calibration was performed across realistic physiological flow ranges using a syringe pump. The device's sensors connected directly to the bilateral femoral veins of swine. For each 1 min, blood flow was monitored, then, an occlusion was introduced, and then, the occlusion was removed to resume flow. Each vein of four pigs was monitored four times, totaling 32 data collections. The implant measured 1.70 cm3 without battery/encapsulation. Across its calibrated range, including equipment tolerances, the relative error is less than ±5% above 8 mL/min and between -0.8% and +1.2% at its largest calibrated flow rate, which to the best of our knowledge is the lowest reported in the literature across the measured calibration range. The average standard deviation of the flow waveform amplitude was three times greater than that of no-flow. Establishing the relative amplitude for the flow and no-flow waveforms was found necessary, particularly for noise modulated Doppler signals. Its size and accuracy, compared with other microcontroller-equipped totally implantable monitors, make it a good candidate for future tether-free free flap monitoring studies.
PDF | DOI: 10.1016/j.bspc.2016.01.012
Swallowing disorders affect thousands of patients every year. Currently utilized techniques to screen for this condition are questionably reliable and are often deployed in non-standard manners, so efforts have been put forth to generate an instrumental alternative based on cervical auscultation. These physiological signals with low signal-to-noise ratios are traditionally denoised by well-known wavelets in a discrete, single tree wavelet decomposition. We attempt to improve this widely accepted method by designing a matched wavelet for cervical auscultation signals to provide better denoising capabilities and by implementing a dual-tree complex wavelet transform to maintain time invariant properties of this filtering. We found that our matched wavelet did offer better denoising capabilities for cervical auscultation signals compared to several popular wavelets and that the dual tree complex wavelet transform did offer better time invariance when compared to the single tree structure. We conclude that this new method of denoising cervical auscultation signals could benefit applications that can spare the required computation time and complexity.
PDF | DOI: 10.3390/s16030393
Wireless energy transfer is a broad research area that has recently become applicable to implantable medical devices. Wireless powering of and communication with implanted devices is possible through wireless transcutaneous energy transfer. However, designing wireless transcutaneous systems is complicated due to the variability of the environment. The focus of this review is on strategies to sense and adapt to environmental variations in wireless transcutaneous systems. Adaptive systems provide the ability to maintain performance in the face of both unpredictability (variation from expected parameters) and variability (changes over time). Current strategies in adaptive (or tunable) systems include sensing relevant metrics to evaluate the function of the system in its environment and adjusting control parameters according to sensed values through the use of tunable components. Some challenges of applying adaptive designs to implantable devices are challenges common to all implantable devices, including size and power reduction on the implant, efficiency of power transfer and safety related to energy absorption in tissue. Challenges specifically associated with adaptation include choosing relevant and accessible parameters to sense and adjust, minimizing the tuning time and complexity of control, utilizing feedback from the implanted device and coordinating adaptation at the transmitter and receiver.
PDF | DOI: 10.1186/s12880-016-0125-0
Background: Functional transcanial Doppler ultrasound (fTCD) is a convenient approach to examine cerebral blood flow velocity (CBFV) in major cerebral arteries. Methods: In this study, the anterior cerebral artery (ACA) was insonated on both sides, that is, right ACA (R-ACA) and left ACA (L-ACA). The envelope signals (the maximum velocity) and the raw signals were analyzed during cognitive processes, i.e. word-generation tasks, geometric tasks and resting state periods separating each task. Data which were collected from 20 healthy participants were used to investigate the changes and the hemispheric functioning while performing cognitive tasks. Signal characteristics were analyzed in time domain, frequency domain and time-frequency domain. Results: Significant results have been obtained through the use of both classic/modern methods (i.e. envelope/raw, time and frequency/information-theoretic and time-frequency domains). The frequency features extracted from the raw signals highlighted sex effects on cerebral blood flow which revealed distinct brain response during each process and during resting periods. In the time-frequency analysis, the distribution of wavelet energies on the envelope signals moved around the low frequencies during mental processes and did not experience any lateralization during cognitive tasks. Conclusions: Even if no lateralization effects were noticed during resting-state, verbal and geometric tasks, understanding CBFV in ACA during cognitive tasks could complement information extracted from cerebral blood flow in middle cerebral arteries during similar cognitive tasks (i.e. sex effects).
PDF | DOI: 10.1186/s12984-015-0110-9
Background: Aspiration, where food or liquid is allowed to enter the larynx during a swallow, is recognized as the most clinically salient feature of oropharyngeal dysphagia. This event can lead to short-term harm via airway obstruction or more long-term effects such as pneumonia. In order to non-invasively identify this event using high resolution cervical auscultation there is a need to characterize cervical auscultation signals from subjects with dysphagia who aspirate. Methods: In this study, we collected swallowing sound and vibration data from 76 adults (50 men, 26 women, mean age 62) who underwent a routine videofluoroscopy swallowing examination. The analysis was limited to swallows of liquid with either thin (less than 5 cps) or viscous (around 300 cps) consistency and was divided into those with deep laryngeal penetration or aspiration (unsafe airway protection), and those with either shallow or no laryngeal penetration (safe airway protection), using a standardized scale. After calculating a selection of time, frequency, and time-frequency features for each swallow, the safe and unsafe categories were compared using Wilcoxon rank-sum statistical tests. Results: Our analysis found that few of our chosen features varied in magnitude between safe and unsafe swallows with thin swallows demonstrating no statistical variation. We also supported our past findings with regard to the effects of sex and the presence or absence of stroke on cervical ausculation signals, but noticed certain discrepancies with regards to bolus viscosity. Conclusions: Overall, our results support the necessity of using multiple statistical features concurrently to identify laryngeal penetration of swallowed boluses in future work with high resolution cervical auscultation.
PDF | DOI: 10.1016/j.jstrokecerebrovasdis.2015.09.005
OBJECTIVES: The primary objective of this paper was to determine whether heart rate variability (HRV) acquired upon admission to inpatient rehabilitation is associated with motor outcome 3 months after stroke. The secondary objective of this paper was to determine whether HRV shows a strong association with the motor outcome 3 months after stroke in individuals with severe initial motor impairments. METHODS: We recruited 13 patients with acute stroke from an acute inpatient rehabilitation hospital. A Holter monitor was placed upon admission and Fugl-Meyer Upper Extremity and Lower Extremity Subscales were used to assess the movement of the affected upper and lower extremities 3 months after admission. The standard deviation of R-R intervals was used to quantify HRV. RESULTS: A Spearman rank correlation revealed a strong positive and significant correlation between HRV upon admission and movement of the affected upper extremity (r=.70, P=.01) and affected lower extremity (r=.60, P=.03) at 3 months. For patients with severe initial motor impairments, HRV showed a strong positive association with the movement of the affected upper (r=.61, P=.04) and lower (r=.70, P=.04) extremities at 3 months. CONCLUSION: HRV is strongly associated with motor outcome after stroke and provides a promising marker to explore the mechanisms associated with motor recovery after stroke.
PDF | DOI: 10.1097/GME.0000000000000481
Objective: Hot flashes are classic symptoms of menopause. Emerging data link hot flashes to cardiovascular disease (CVD) risk, yet whether hot flashes are related to brain health is poorly understood. We examined the relationship between hot flashes (measured via physiologic monitor and self-report) and white matter hyperintensities (WMH) among midlife women. Methods: Twenty midlife women (aged 40-60 y) without clinical CVD, with an intact uterus and ovaries, and not taking hormone therapy were recruited. Women underwent 24 hours of ambulatory physiologic and diary hot flash monitoring to quantify hot flashes; magnetic resonance imaging to assess WMH burden; 72 hours of actigraphy to quantify sleep; and a blood draw, questionnaires, and physical measures to quantify demographics and CVD risk factors. Tests of a priori hypotheses regarding relationships between physiologically monitored and self-reported wake and sleep hot flashes and WMH were conducted in linear regression models. Results: More physiologically monitored hot flashes during sleep were associated with greater WMH, controlling for age, race, and body mass index ([beta] [SE] = 0.0002 [0.0001], P = 0.03]. Findings persisted after controlling for sleep characteristics and additional CVD risk factors. No relationships were observed for self-reported hot flashes. Conclusions: More physiologically monitored hot flashes during sleep are associated with greater WMH burden among midlife women without clinical CVD. Results suggest that the relationship between hot flashes and CVD risk observed in the periphery may extend to the brain. Future work should consider the unique role of sleep hot flashes in brain health.
PDF | DOI: 10.1109/JTEHM.2015.2504961
Objective: evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method: we proposed a method to extract stride cycle events from tri-axial accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson's disease, and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results: our analysis of the accelerometry data showed that the proposed methodology was able to accurately extract heel and toe-contact events from both feet. We used t-tests, analysis of variance (ANOVA) and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture, and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be similarly identified using measures with the two methods. Conclusions: a simple tri-axial acceleromter accompanied by a signal processing algorithm can be used to capture stride events. Clinical impact: the proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step toward the assessment of stride events using tri-axial accelerometers in real-life settings.
PDF | DOI: 10.1016/j.cmpb.2015.08.012
Gait function is traditionally assessed using well-lit, unobstructed walkways with minimal distractions. In patients with subclinical physiological abnormalities, these conditions may not provide enough stress on their ability to adapt to walking. The introduction of challenging walking conditions in gait can induce responses in physiological systems in addition to the locomotor system. There is a need for a device that is capable of monitoring multiple physiological systems in various walking conditions. To address this need, an Android-based gait-monitoring device was developed that enabled the recording of a patient's physiological systems during walking. The gait-monitoring device was tested during self-regulated overground walking sessions of fifteen healthy subjects that included 6 females and 9 males aged 18–35 years. The gait-monitoring device measures the patient's stride interval, acceleration, electrocardiogram, skin conductance and respiratory rate. The data is stored on an Android phone and is analyzed offline through the extraction of features in the time, frequency and time–frequency domains. The analysis of the data depicted multisystem physiological interactions during overground walking in healthy subjects. These interactions included locomotion-electrodermal, locomotion-respiratory and cardiolocomotion couplings. The current results depicting strong interactions between the locomotion system and the other considered systems (i.e., electrodermal, respiratory and cardiovascular systems) warrant further investigation into multisystem interactions during walking, particularly in challenging walking conditions with older adults.
PDF | DOI: 10.1007/s11571-015-9355-z
A spinal cord injury (SCI) is one of the most common neurological disorders. In this paper, we examined the consequences of upper SCI in a male participant on the cerebral blood flow velocity. In particular, transcranial Doppler was used to study these effects through middle cerebral arteries (MCA) during resting-state periods and during cognitive challenges (non-verbal word-generation tasks and geometric-rotation tasks). Signal characteristics were analyzed from raw signals and envelope signals (maximum velocity) in the time domain, the frequency domain and the time–frequency domain. The frequency features highlighted an increase of the peak frequency in L-MCA and R-MCA raw signals, which revealed stronger cerebral blood flow during geometric/verbal processes respectively. This underlined a slight dominance of the right hemisphere during word-generation periods and a slight dominance of the left hemisphere during geometric processes. This finding was confirmed by cross-correlation in the time domain and by the entropy rate in information-theoretic domain. A comparison of our results to other neurological disorders (Alzheimer’s disease, Parkinson’s disease, autism, epilepsy, traumatic brain injury) showed that the SCI had similar effects such as general decreased cerebral blood flow and similar regular hemispheric dominance in a few cases.
PDF | DOI: 10.1016/j.jelectrocard.2015.08.014
Background: The serum rise of cardiac troponin remains the gold standard for diagnosing non-ST elevation (NSTE) myocardial infarction (MI) despite its delayed response. Novel methods for real-time detection of NSTEMI would result in more immediate initiation of definitive medical therapy and faster transport to facilities that can provide specialized cardiac care. Methods: EMPIRE is an ongoing prospective, observational cohort study designed to quantify the magnitude of ischemia-induced repolarization dispersion for the early detection of NSTEMI. In this ongoing study, prehospital ECG data is gathered from patients who call 9-1-1 with a chief complaint of non-traumatic chest pain. This data is then analyzed using the principal component analysis (PCA) technique of 12-lead ECGs to fully characterize the spatial and temporal qualities of STT waveforms. Results: Between May and December of 2013, Pittsburgh EMS obtained and transmitted 351 prehospital ECGs of the 1149 patients with chest pain-related emergency dispatches transported to participating hospitals. After excluding those with poor ECG signal (n = 40, 11%) and those with pacing or LBBB (n = 50, 14%), there were 261 eligible patients (age 57 ± 16 years, 45% female, 45% Black). In this preliminary sample, there were 19 STEMI (7%) and 33 NSTEMI (12%). More than 50% of those with infarction (STEMI or NSTEMI) had initially negative troponin values upon presentation. We present ECG data of such NSTEMI case that was identified correctly using our methods. Conclusions: Concrete ECG algorithms that can quantify NSTE ischemia and allow differential treatment based on such ECG changes could have an immediate clinical impact on patient outcomes. We describe the rationale, development, design, and potential usefulness of the EMPIRE study. The findings may provide insights that can influence guidelines revisions and improve public health.
PDF | DOI: 10.1109/TBME.2015.2431999
Objective: The effects of the chin-tuck maneuver, a technique commonly employed to compensate for dysphagia, on cervical auscultation are not fully understood. Characterizing a technique that is known to affect swallowing function is an important step on the way to developing a new instrumentationbased swallowing screening tool. Methods: In this study, we recorded data from 55 adult participants who each completed five saliva swallows in a chin-tuck position. The resulting data was processed using previously designed filtering and segmentation algorithms. We then calculated 9 time, frequency, and timefrequency domain features for each independent signal. Results: We found that multiple frequency and time domain features varied significantly between male and female subjects as well as between swallowing sounds and vibrations. However, our analysis showed that participant age did not play a significant role on the values of the extracted features. Finally, we found that various frequency features corresponding to swallowing vibrations did demonstrate statistically significant variation between the neutral and chin-tuck positions but sounds showed no changes between these two positions. Conclusion: The chin-tuck maneuver affects many facets of swallowing vibrations and sounds and its effects can be monitored via cervical auscultation. Significance: These results suggest that a subject’s swallowing technique does need to be accounted for when monitoring their performance with cervical auscultation based instrumentation.
PDF | DOI: 10.1088/1741-2560/12/5/051001
Swallowing and swallowing disorders have garnered continuing interest over the past several decades. Electroencephalography (EEG) is an inexpensive and non-invasive procedure with very high temporal resolution which enables analysis of short and fast swallowing events, as well as an analysis of the organizational and behavioral aspects of cortical motor preparation, swallowing execution and swallowing regulation. EEG is a powerful technique which can be used alone or in combination with other techniques for monitoring swallowing, detection of swallowing motor imagery for diagnostic or biofeedback purposes, or to modulate and measure the effects of swallowing rehabilitation. This paper provides a review of the existing literature which has deployed EEG in the investigation of oropharyngeal swallowing, smell, taste and texture related to swallowing, cortical pre-motor activation in swallowing, and swallowing motor imagery detection. Furthermore, this paper provides a brief review of the different modalities of brain imaging techniques used to study swallowing brain activities, as well as the EEG components of interest for studies on swallowing and on swallowing motor imagery. Lastly, this paper provides directions for future swallowing investigations using EEG.
PDF | DOI: 10.1186/s12993-015-0073-9
Gait is a complex process involving both cognitive and sensory ability and is strongly impacted by the environment. In this paper, we propose to study of the impact of a cognitive task during gait on the cerebral blood flow velocity, the blood flow signal features and the correlation of gait and blood flow features through a dual task methodology. Both cerebral blood flow velocity and gait characteristics of eleven participants with no history of brain or gait conditions were recorded using transcranial Doppler on mid-cerebral artery while on a treadmill. The cognitive task was induced by a backward counting starting from 10,000 with decrement of 7. Central blood flow velocity raw and envelope features were extracted in both time, frequency and time-scale domain; information-theoretic metrics were also extracted and statistical significances were inspected. A similar feature extraction was performed on the stride interval signal. Statistical differences between the cognitive and baseline trials, between the left and right mid-cerebral arteries signals and the impact of the antropometric variables where studied using linear mixed models. No statistical differences were found between the left and right mid-cerebral arteries flows or the baseline and cognitive state gait features, while statistical differences for specific features were measured between cognitive and baseline states. These statistical differences found between the baseline and cognitive states show that cognitive process has an impact on the cerebral activity during walking. The state was found to have an impact on the correlation between the gait and blood flow features.
PDF | DOI: 10.1166/jolpe.2015.1399
Passive RFID is rapidly expanding for asset tracking while simultaneously opening broader fronts in passive communications and sensing as a platform technology. As technology improves and power requirements are lowered, the possibilities for increased flexibility bring about new “RFID” opportunities with active functionality (e.g., a Passive Camera). In addition, new manufacturing technologies can now provide flexibility and cost advantages for early stage developers when compared to ASIC ICs. This paper addresses a potential security application and other applications including passive camera technologies, inter-chip systems communications on an RFID tag, and new core prototyping and manufacturing technologies to accelerate development in the total RFID space.
PDF | DOI: 10.1109/THMS.2015.2408615
Cervical auscultation is the recording of sounds and vibrations caused by the human body from the throat during swallowing. While traditionally done by a trained clinician with a stethoscope, much work has been put toward developing more sensitive and clinically useful methods to characterize the data obtained with this technique. The eventual goal of the field is to improve the effectiveness of screening algorithms designed to predict the risk that swallowing disorders pose to individual patients’ health and safety. This paper provides an overview of these signal-processing techniques and summarizes recent advances made with digital transducers in hopes of organizing the highly varied research on cervical auscultation. It investigates where on the body these transducers are placed in order to record a signal as well as the collection of analog and digital filtering techniques used to further improve the signal quality. It also presents the wide array of methods and features used to characterize these signals, ranging from simply counting the number of swallows that occur over a period of time to calculating various descriptive features in the time, frequency, and phase space domains. Finally, this paper presents the algorithms that have been used to classify these data into “normal” and “abnormal” categories. Both linear as well as nonlinear techniques are presented in this regard.
PDF | DOI: 10.1186/s12984-015-0049-x
Transcranial Doppler (TCD) recordings are used to monitor cerebral blood ow in main cerebral arteries. The resting state is usually characterized by using the mean velocity or the maximum Doppler shift frequency (an envelope signal) by insonating the middle cerebral arteries (MCAs). In this study, we characterized the cerebral blood ow in the anterior cerebral arteries (ACAs). We analyzed both the envelope signals and the raw signals obtained from bilateral insonation. We recruited 20 healthy subjects and conducted the data acquisition for 15 minutes. Features were extracted from the time domain, the frequency domain and the time-frequency domain. The results showed that gender-based statistical dierence exists in the frequency domain and the time-frequency domain. However, no handedness eect was found. In the time domain, the information-theoretic features showed that the mutual dependence is higher in raw signals than in envelope signals. Finally, we concluded that insonating the ACA will serve as a complement of the MCA studies. Additionally, the investigation of the raw signals provided us with additional information that is not otherwise available from the envelope signals. The direct TCD raw-data utilization is therefore validated as a valuable resting-state characterization method.
PDF | DOI: 10.4236/ojvm.2015.57022
A valid non human primate model of traumatic spinal cord injury (TSCI) is essential to evaluate and develop new treatments. In previous experiments, it has been demonstrated that a transmitter can be implanted in the macaque fasicularis monkey that measures electromyographic data from the musculature of the tail. As well, previous experiments have demonstrated that selective lesions can be created in the lower thoracic spinal cord that does not cause limb weakness and/or bowel dysfunction. The histopathological features of these lesions appear similar to human TSCI. This paper describes a method by which the EMG data can be transformed into a quantitative metric of volitional limb movement (“Q”). This metric permits an assessment an objective assessment of injury, natural recovery as well as potential efficacy of candidate treatments.
PDF | DOI: 10.1097/PRS.0000000000001372
Microvascular anastomotic failure remains an uncommon but devastating problem. Although the implantable Doppler probe is helpful in flap monitoring, the devices are cumbersome, easily dislodged, and plagued by false-positive results. The authors have developed an implantable wireless Doppler monitor prototype from off-the-shelf components and tested it in a swine model. The wireless probe successfully distinguished between femoral vein flow, occlusion, and reflow, and wirelessly reported the different signals reliably. This is the first description of a wireless implantable blood flow sensor for flap monitoring. Future iterations will incorporate an integrated microchip-based Doppler system that will decrease the size to 1 mm2, small enough to fit onto an anastomotic coupler.
PDF | DOI: 10.1016/j.compbiomed.2015.03.027
Gait accelerometry is an important approach for gait assessment. Previous contributions have adopted various pre-processing approaches for gait accelerometry signals, but none have thoroughly investigated the effects of such pre-processing operations on the obtained results. Therefore, this paper investigated the influence of pre-processing operations on signal features extracted from gait accelerometry signals. These signals were collected from 35 participants aged over 65 years: 14 of them were healthy controls (HC), 10 had Parkinson׳s disease (PD) and 11 had peripheral neuropathy (PN). The participants walked on a treadmill at preferred speed. Signal features in time, frequency and time–frequency domains were computed for both raw and pre-processed signals. The pre-processing stage consisted of applying tilt correction and denoising operations to acquired signals. We first examined the effects of these operations separately, followed by the investigation of their joint effects. Several important observations were made based on the obtained results. First, the denoising operation alone had almost no effects in comparison to the trends observed in the raw data. Second, the tilt correction affected the reported results to a certain degree, which could lead to a better discrimination between groups. Third, the combination of the two pre-processing operations yielded similar trends as the tilt correction alone. These results indicated that while gait accelerometry is a valuable approach for the gait assessment, one has to carefully adopt any pre-processing steps as they alter the observed findings.
PDF | DOI: 10.1016/j.fertnstert.2015.03.008
Objective: To test whether more physiologically assessed hot flashes were associated with more connectivity in the default mode network (DMN), the network of brain regions active during rest. We particularly focus on DMN networks supporting the hippocampus as this region is rich in estrogen (E) receptors (ER) and has previously been linked to hot flashes. Design: Women underwent 24 hours of physiologic and diary hot flash monitoring, functional magnetic resonance imaging (MRI), 72 hours of sleep actigraphy monitoring, a blood draw, questionnaires, and physical measures. Setting: University medical center. Patients: Twenty midlife women aged 40–60 years who had their uterus and both ovaries and were not taking hormone therapy (HT). Interventions: None. Main Outcome Measure(s): The DMN functional connectivity. Results: Controlling for age, race, and education, more physiologically-monitored hot flashes were associated with greater DMN connectivity (beta, B [SE] = 0.004 [0.002]), particularly hippocampal DMN connectivity (B [SE] = 0.005 [0.002]). Findings were most pronounced for sleep physiologic hot flashes (with hippocampal DMN, B [SE] = 0.02 [0.007]). Associations also persisted controlling for sleep, depressive symptoms, and serum E2 concentrations. Conclusions: More physiologically-monitored hot flashes were associated with more DMN connectivity, particularly networks supporting the hippocampus. Findings were most pronounced for sleep hot flashes. Findings underscore the importance of continued investigation of the central nervous system in efforts to understand this classic menopausal phenomenon.
PDF | DOI: 10.1016/j.compbiomed.2015.01.007
Background: Cervical auscultation with high resolution sensors is currently under consideration as a method of automatically screening for specific swallowing abnormalities. To be clinically useful without human involvement, any devices based on cervical auscultation should be able to detect specified swallowing events in an automatic manner. Methods: In this paper, we comparatively analyze the density-based spatial clustering of applications with noise algorithm (DBSCAN), a k-means based algorithm, and an algorithm based on quadratic variation as methods of differentiating periods of swallowing activity from periods of time without swallows. These algorithms utilized swallowing vibration data exclusively and compared the results to a gold standard measure of swallowing duration. Data was collected from 23 subjects that were actively suffering from swallowing difficulties. Results: Comparing the performance of the DBSCAN algorithm with a proven segmentation algorithm that utilizes k-means clustering demonstrated that the DBSCAN algorithm had a higher sensitivity and correctly segmented more swallows. Comparing its performance with a threshold-based algorithm that utilized the quadratic variation of the signal showed that the DBSCAN algorithm offered no direct increase in performance. However, it offered several other benefits including a faster run time and more consistent performance between patients. All algorithms showed noticeable differentiation from the endpoints provided by a videofluoroscopy examination as well as reduced sensitivity. Conclusions: In summary, we showed that the DBSCAN algorithm is a viable method for detecting the occurrence of a swallowing event using cervical auscultation signals, but significant work must be done to improve its performance before it can be implemented in an unsupervised manner.
PDF | DOI: 10.1038/518483a
A short paper.
PDF | DOI: 10.1186/1475-925X-14-3
Background: Accelerometry (the measurement of vibrations) and auscultation (the measurement of sounds) are both non-invasive techniques that have been explored for their potential to detect abnormalities in swallowing. The differences between these techniques and the information they capture about swallowing have not previously been explored in a direct comparison. Methods: In this study, we investigated the differences between dual-axis swallowing accelerometry and swallowing sounds by recording data from adult participants and calculating a number of time and frequency domain features. During the experiment, 55 participants (ages 18-65) were asked to complete five saliva swallows in a neutral head position. The resulting data was processed using previously designed techniques including wavelet denoising, spline filtering, and fuzzy means segmentation. The pre-processed signals were then used to calculate 9 time, frequency, and timefrequency domain features for each independent signal. Wilcoxon signed-rank and Wilcoxon ranksum tests were utilized to compare feature values across transducers and patient demographics, respectively. Results: In addition to finding a number of features that varied between male and female participants, our statistical analysis determined that the majority of our chosen features were statistically significantly different across the two sensor methods and that the dependence on within-subject factors varied with the transducer type. However, a regression analysis showed that age accounted for an insignificant amount of variation in our signals. Conclusions: We conclude that swallowing accelerometry and swallowing sounds provide different information about deglutition despite utilizing similar transduction methods. This contradicts past assumptions in the field and necessitates the development of separate analysis and processing techniques for swallowing sounds and vibrations.
PDF | DOI: 10.1002/ente.201402067
This paper reviews the techniques of below ground wireless communication in the oil and gas industry. A historical and theoretical analysis of pressure wave and electromagnetic communication is presented. Case studies for both technologies and their current applications are evaluated for the purpose of identifying each method’s limitations and opportunities for innovation. Finally, the possibilities of smart well technology are discussed with focus on wirelessly powered sensors for continuous monitoring of shale oil/gas reservoirs using electromagnetic methods. We conclude that the critical challenges are associated with powering the devices, which must perform for a period of months to years and which must be capable of generating sufficiently powerful signals so as to overcome the large signal attenuation associated with electromagnetic wave propagation through geological media.
PDF | DOI: 10.1177/1545968314556284
A short paper.
PDF | DOI: 10.1016/j.brainres.2014.09.035
Electroencephalography (EEG) systems can enable us to study cerebral activation patterns during performance of swallowing tasks and possibly infer about the nature of abnormal neurological conditions causing swallowing difficulties. While it is well known that EEG signals are non-stationary, there are still open questions regarding the stationarity of EEG during swallowing activities and how the EEG stationarity is affected by different viscosities of the fluids that are swallowed by subjects during these swallowing activities. In the present study, we investigated the EEG signal collected during swallowing tasks by collecting data from 55 healthy adults (ages 18-65). Each task involved the deliberate swallowing of boluses of fluids of different viscosities. Using time-frequency tests with surrogates, we showed that the EEG during swallowing tasks could be considered non-stationary. Furthermore, the statistical tests and linear regression showed that the parameters of fluid viscosity, sex, and different brain regions significantly influenced the index of non-stationarity values. Therefore, these parameters should be considered in future investigations which use EEG during swallowing activities.
PDF | DOI: 10.1097/01.prs.0000455398.99175.a6
A short paper.
The strength of time-dependent correlations known as stride interval (SI) dynamics has been proposed as an indicator of neurologically healthy gait. Most recently, it has been hypothesized that these dynamics may be necessary for gait efficiency although the supporting evidence to date is scant. The current study examines over-ground SI dynamics, and their relationship with the cost of walking and physical activity levels in neurologically healthy children aged nine to 15 years. Twenty participants completed a single experimental session consisting of three phases: 10 minutes resting, 15 minutes walking and 10 minutes recovery. The scaling exponent (alpha) was used to characterize SI dynamics while net energy cost was measured using a portable metabolic cart, and physical activity levels were determined based on a 7-day recall questionnaire. No significant linear relationships were found between alpha and the net energy cost measures (r<0.07; p>0.25) or between alpha and physical activity levels (r=0.01, p=0.62). However, there was a marked reduction in the variance of alpha as activity levels increased. Over-ground stride dynamics do not appear to directly reflect energy conservation of gait in neurologically healthy youth. However, the reduction in the variance of alpha with increasing physical activity suggests a potential exercise-moderated convergence towards a level of stride interval persistence for able-bodied youth reported in the literature. This latter finding warrants further investigation.
PDF | DOI: 10.1016/j.neures.2014.02.009
Functional transcranial Doppler (fTCD) is a useful medical imaging technique to monitor cerebral blood flow velocity (CBFV) in major cerebral arteries. In this paper, CBFV changes in the right and left middle cerebral arteries (MCA) caused by cognitive tasks, such as word generation tasks and mental rotation tasks, were examined using fTCD. CBFV recordings were collected from 20 healthy subjects (10 females, 10 males). We obtained both the raw CBFV signal and the envelope CBFV signal, which is the maximal velocity to gain more information about the changes and hemisphere lateralization in cognitive tasks compared to the resting state. Time, frequency, time-frequency, and information-theoretic features were calculated and compared. Sex effects were also taken into consideration. The results of our analysis demonstrated that the raw CBFV signal contained more descriptive information than the envelope signals. Furthermore, both types of cognitive tasks produced higher values in most signal features. Geometric tasks were more distinguished from the rest-state than verbal tasks and the lateralization was exhibited in right MCA during geometric tasks. Our results show that the raw CBFV signals provided valuable information when studying the effects of cognitive tasks and lateralization in the MCA.
PDF | DOI: 10.1371/journal.pone.0099318
Decline in cognitive performance is associated with gait deterioration. Our objectives were: 1) to determine, from an original study in older community-dwellers without diagnosis of dementia, which gait parameters, among slower gait speed, higher stride time variability (STV) and Timed Up & Go test (TUG) delta time, were most strongly associated with lower performance in two cognitive domains (i.e., episodic memory and executive function); and 2) to quantitatively synthesize, with a systematic review and meta-analysis, the association between gait performance and cognitive decline (i.e., mild cognitive impairment (MCI) and dementia). Based on a cross-sectional design, 934 older community-dwellers without dementia (mean±standard deviation, 70.3±4.9years; 52.1% female) were recruited. A score at 5 on the Short Mini-Mental State Examination defined low episodic memory performance. Low executive performance was defined by clock-drawing test errors. STV and gait speed were measured using GAITRite system. TUG delta time was calculated as the difference between the times needed to perform and to imagine the TUG. Then, a systematic Medline search was conducted in November 2013 using the Medical Subject Heading terms "Delirium," "Dementia," "Amnestic," "Cognitive disorders" combined with "Gait" OR "Gait disorders, Neurologic" and "Variability." A total of 294 (31.5%) participants presented decline in cognitive performance. Higher STV, higher TUG delta time, and slower gait speed were associated with decline in episodic memory and executive performances (all P-values <0.001). The highest magnitude of association was found for higher STV (effect size = −0.74 [95% Confidence Interval (CI): −1.05;−0.43], among participants combining of decline in episodic memory and in executive performances). Meta-analysis underscored that higher STV represented a gait biomarker in patients with MCI (effect size = 0.48 [95% CI: 0.30;0.65]) and dementia (effect size = 1.06 [95% CI: 0.40;1.72]). Conclusion Higher STV appears to be a motor phenotype of cognitive decline.
PDF | DOI: 10.1049/iet-spr.2013.0398
Unlike synchronous processing, low–power asynchronous processing is more efficient in biomedical and sensing networks applications as it it is free from aliasing constraints and quantization error in the amplitude, it allows continuous–time processing and more importantly data is only acquired in significant parts of the signal. In this paper, we consider signal decomposers based on the asynchronous sigma delta modulator (ASDM), a non–linear feedback system that maps the signal amplitude into the zero–crossings of a binary output signal. The input, the zero–crossings and the ASDM parameters are related by an integral equation making the signal reconstruction difficult to implement. Modifying the model for the ASDM, we obtain a recursive equation that permits to obtain the non–uniform samples from the zero–time crossing values. Latticing the joint time–frequency space into defined frequency bands, and time windows depending on the scale parameter different decompositions, similar to wavelet decompositions, are possible. We present two cascade low– and high–frequency decomposers, and a bank–of–filters parallel decomposer. This last decomposer using the modified ASDM behaves like a asynchronous analog to digital converter, and using an interpolator based on Prolate Spheroidal Wave functions allows reconstruction of the original signal. The asynchronous approaches proposed here are well suited for processing signals sparse in time, and for low–power applications. The different approaches are illustrated using synthetic and actual signals.
PDF | DOI: 10.1049/iet-spr.2013.0402
An implantable wireless Doppler device used in microsurgical free flap surgeries can suffer from lost data points. In order to recover the lost samples, we considered approaches based on recently proposed compressive sensing. In this paper, we carried out a comparative analysis of several different approaches using synthetic signals and real signals obtained during blood flow monitoring in four pigs. We considered three different basis functions: Fourier basis, discrete prolate spheroidal sequences and modulated discrete prolate spheroidal sequences. To avoid the computational burden, we considered approaches based on the $l_1$ minimization for all three bases. To understand the trade-off between the computational complexity and the accuracy, we also used a recovery process based on matching pursuit and modulated discrete prolate spheroidal sequences bases. For both synthetic and real signals, the matching approach with modulated discrete prolate spheroidal sequences provided the most accurate results. Future studies should focus on the optimization of the modulated discrete prolate spheroidal sequences in order to further decrease the computational complexity and increase the accuracy.
PDF | DOI: 10.1109/TNSRE.2013.2265887
Gait accelerometry is a promising tool to assess human walking and reveal deteriorating gait characteristics in patients and can be a rich source of clinically relevant information about functional declines in older adults. Therefore, in this paper, we propose a comprehensive set of signal features that may be used to extract clinically valuable information from gait accelerometry signals. To achieve our goal, we collected tri-axial gait accelerometry signals from 35 adults 65 years of age and older. Fourteen subjects were healthy controls, ten participants were diagnosed with Parkinson's disease and eleven participants were diagnosed with peripheral neuropathy. The data were collected while the participants walked on a treadmill at a preferred walking speed. Accelerometer signal features in time, frequency and time-frequency domains were extracted. The results of our analysis showed that some of the extracted features were able to differentiate between healthy and clinical populations. Signal features in all three domains were able to emphasize variability among different groups, and also revealed valuable information about variability of the signals between anterior-posterior, mediolateral and vertical directions within subjects. The current results imply that the proposed signal features can be valuable tools for the analysis of gait accelerometry data and should be utilized in future studies.
PDF | DOI: 10.1186/1743-0003-11-66
Objectives: 1) To measure and compare the time required to perform (pTUG) and the time required to imagine (iTUG) the Timed Up & Go (TUG), and the time difference between these two tasks (i.e., TUG delta time) in older adults with cognitive decline (i.e., mild cognitive impairment (MCI) and mild-to-moderate Alzheimer disease and related disorders (ADRD)) and in cognitively healthy individuals (CHI); and 2) to examine any association between the TUG delta time and a cognitive status. Methods: Sixty-six participants (24 CHI, 23 individuals with MCI, and 19 individuals with ADRD) were recruited in this cross-sectional study. The mean and standard deviation of the pTUG and iTUG completion times and the TUG delta time, as well as age, gender, and Mini-Mental State Examination (MMSE) scores were used as outcomes. Participants were separated into three groups based on the tertilization of TUG delta time: lowest (<13.6%; n = 22; best performance), intermediate (13.6-52.2%; n = 22), and highest tertile (>52.2%; n = 22, worst performance). Results: Fewer CHI were in the group exhibiting the highest tertile of TUG delta time compared to individuals with lowest and intermediate TUG delta times (p = 0.013). Being in the highest tertile of the TUG delta time was associated with cognitive decline in the unadjusted model (p = 0.012 for MCI, and p = 0.021 for mild-to-moderate ADRD). In the multivariate models, this association remained significant only for individuals with MCI (p = 0.019 while adjusting for age and gender; p = 0.047 while adjusting for age, gender, and MMSE score; p = 0.012 for the stepwise backward model). Conclusions: Our results provide the first evidence that motor imagery of gait may be used as a biomarker of MCI in older adults.
PDF | DOI: 10.1016/j.jneumeth.2014.02.003
Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks. In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity. Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition. The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data.
PDF | DOI: 10.1038/srep04468
The ability to accurately measure real-time pH fluctuations in-vivo could be highly advantageous. Early detection and potential prevention of bacteria colonization of surgical implants can be accomplished by monitoring associated acidosis. However, conventional glass membrane or ion-selective field-effect transistor (ISFET) pH sensing technologies both require a reference electrode which may suffer from leakage of electrolytes and potential contamination. Herein, we describe a solid-state sensor based on oxidized single-walled carbon nanotubes (ox-SWNTs) functionalized with the conductive polymer poly(1-aminoanthracene) (PAA). This device had a Nernstian response over a wide pH range (2–12) and retained sensitivity over 120 days. The sensor was also attached to a passively-powered radio-frequency identification (RFID) tag which transmits pH data through simulated skin. This battery-less, reference electrode free, wirelessly transmitting sensor platform shows potential for biomedical applications as an implantable sensor, adjacent to surgical implants detecting for infection.
PDF | DOI: 10.1038/507306a
A short paper.
PDF | DOI: 10.1097/01.prs.0000444934.85733.e9
A short paper.
PDF | DOI: 10.1016/j.jneumeth.2013.10.017
Background: The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f) = 1/f^beta. The scaling exponent beta is thus often interpreted as a ``biomarker'' of relative health and decline. New Method: This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. Results: The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with Existing Methods: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. Conclusions: The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index $\beta$. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series.
PDF | DOI: 10.1016/j.ultrasmedbio.2013.06.016
Transcranial Doppler (TCD) recordings are used to monitor cerebral blood ow in main cerebral arteries. The resting state is usually characterized by using the mean velocity or the maximum Doppler shift frequency (an envelope signal) by insonating the middle cerebral arteries (MCAs). In this study, we characterized the cerebral blood ow in the anterior cerebral arteries (ACAs). We analyzed both the envelope signals and the raw signals obtained from bilateral insonation. We recruited 20 healthy subjects and conducted the data acquisition for 15 minutes. Features were extracted from the time domain, the frequency domain and the time-frequency domain. The results showed that gender-based statistical dierence exists in the frequency domain and the time-frequency domain. However, no handedness eect was found. In the time domain, the information-theoretic features showed that the mutual dependence is higher in raw signals than in envelope signals. Finally, we concluded that insonating the ACA will serve as a complement of the MCA studies. Additionally, the investigation of the raw signals provided us with additional information that is not otherwise available from the envelope signals. The direct TCD raw-data utilization is therefore validated as a valuable resting-state characterization method.
PDF | DOI: 10.1186/1475-925X-12-90
Background: Cervical auscultation (CA) is an affordable, non-invasive technique used to observe sounds occurring during swallowing. CA involves swallowing characterization via stethoscopes or microphones, while accelerometers can detect other vibratory signals. While the effects of fluid viscosity on swallowing accelerometry signals is well understood, there are still open questions about these effects on swallowing sounds. Therefore, this study investigated the influence of fluids with increasing thickness on swallowing sound characteristics. Method: We collected swallowing sounds and swallowing accelerometry signals from 56 healthy participants. Each participant completed five water swallows, five swallows of nectar-thick apple juice, and five swallows of honey-thick apple juice. These swallows were completed in neutral head and chin-tuck head positions. After pre-processing of collected signals, a number of features in time, frequency and time-frequency domains were extracted. Results: Our numerical analysis demonstrated that significant influence of viscosity was found in most of the features. In general, features extracted from swallows in the neutral head position were affected more than swallows from the chin-tuck position. Furthermore, most of the differences were found between water and fluids with higher viscosity. Almost no significant differences were found between swallows involving nectar-thick and honey-thick apple juices. Our results also showed that thicker fluids had higher acoustic regularity and predictability as demonstrated by the information-theoretic features, and a lower frequency content as demonstrated by features in the frequency domain. Conclusions: According to these results, we can conclude that viscosity of fluids should be considered in future investigations involving swallowing sounds.
Non–stationarity relates to the variation over time of the statistics of a signal. Therefore, signals from practical applications which are realizations of non–stationary processes are difficult to represent and to process. In this paper, we provide a comprehensive discussion of the asynchronous representation and processing of non–stationary signals using a time-frequency framework. Power consumption and type of processing imposed by the size of the devices in many applications motivate the use of asynchronous, rather than conventional synchronous, approaches. This leads to the consideration of non–uniform, signal–dependent level–crossing and asynchronous sigma delta modulator (ASDM) based sampling. Reconstruction from a non– uniform sampled signal is made possible by connecting the sinc and the Prolate Spheroidal Wave (PSW) functions — a more appropriate basis. Two decomposition procedures are considered. One is based on the ASDM that generalizes the Haar wavelet representation and is used for representing analog non–stationary signals. The second decomposer is for representing discrete non– stationary signals. It is based on a linear-chirp based transform that provides local time-frequency parametric representations based on linear chirps as intrinsic mode functions. Important applications of these procedures are the compression and processing of biomedical signals as it will be illustrated.
PDF | DOI: 10.1016/j.mehy.2013.07.040
The worldwide incidence of traumatic spinal cord injury (SCI) is approximated at 180,000 new cases per year. Experiments using nonhuman primates (NHP) are often used to replicate the human condition in order to advance the understanding of SCI and to assist in the development of new treatments. Experimental spinal cord lesions in NHP have been created by a number of methods including blunt trauma, epidural balloons, circumferential cuffs, and dropping a precision weight over the spinal cord. As well, experimental lesions have been created with sharp instruments after opening the dura mater. However, spinal cord lesions that are created with a sharp instrument in NHP experiments may not replicate the clinical and pathological features of human spinal cord injury. Researchers should recognize the challenges associated with making clinical inferences in human SCIs based on NHP experiments that created experimental lesions with a sharp surgical instrument.
PDF | DOI: 10.1016/j.compbiomed.2013.07.019
This paper presents a two-part study with walking conditions involving music and television (TV) to investigate their effects on human gait. In the first part, we observed seventeen able-bodied adults as they participated in three 15-minute walking trials: (1) without music, (2) with music and (3) without music again. In the second part, we observed fifteen able-bodied adults as they walked on a treadmill for 15 min while watching (1) TV with sound (2) TV without sound and (3) TV with subtitles but no sound. Gait timing was recorded via bilateral heel sensors and center-of-mass accelerations were measured by tri-axial accelerometers. Measures of statistical persistence, dynamic stability and gait variability were calculated. Our results showed that none of the considered gait measures were statistically different when comparing music with no-music trials. Therefore, walking to music did not appear to affect intrinsic walking dynamics in the able-bodied adult population. However, stride interval variability and stride interval dynamics were significantly greater in the TV with sound walking condition when compared to the TV with subtitles condition. Treadmill walking while watching TV with subtitles alters intrinsic gait dynamics but potentially offers greater gait stability.
PDF | DOI: 10.1186/1475-925X-12-90
Background: Cervical auscultation (CA) is an affordable, non-invasive technique used to observe sounds occurring during swallowing. CA involves swallowing characterization via stethoscopes or microphones, while accelerometers can detect other vibratory signals. While the effects of fluid viscosity on swallowing accelerometry signals is well understood, there are still open questions about these effects on swallowing sounds. Therefore, this study investigated the influence of fluids with increasing thickness on swallowing sound characteristics. Method: We collected swallowing sounds and swallowing accelerometry signals from 56 healthy participants. Each participant completed five water swallows, five swallows of nectar-thick apple juice, and five swallows of honey-thick apple juice. These swallows were completed in neutral head and chin-tuck head positions. After pre-processing of collected signals, a number of features in time, frequency and time-frequency domains were extracted. Results: Our numerical analysis demonstrated that significant influence of viscosity was found in most of the features. In general, features extracted from swallows in the neutral head position were affected more than swallows from the chin-tuck position. Furthermore, most of the differences were found between water and fluids with higher viscosity. Almost no significant differences were found between swallows involving nectar-thick and honey-thick apple juices. Our results also showed that thicker fluids had higher acoustic regularity and predictability as demonstrated by the information-theoretic features, and a lower frequency content as demonstrated by features in the frequency domain. Conclusions: According to these results, we can conclude that viscosity of fluids should be considered in future investigations involving swallowing sounds.
PDF | DOI: 10.1007/s10439-013-0873-8
Translational research has recently been rediscovered as one of the basic tenants of engineering. Although many people have numerous ideas of how to accomplish this successfully, the fundamental method is to provide an innovative and creative environment. The University of Pittsburgh has been accomplishing this goal though a variety of methodologies. The contents of this paper are exemplary of what can be achieved though the interaction of students, staff, faculty and, in one example, high school teachers. While the projects completed within the groups involved in this paper have spanned other areas, the focus of this paper is on the biomedical devices, that is, towards improving and maintaining health in a variety of areas. The spirit of the translational research is discovery, invention, intellectual property protection, and the creation of value through the spinning off of companies while providing better health care and creating jobs. All but one of these projects involve wireless radio frequency (RF) energy for delivery. The remaining device can be wirelessly connected for data collection.
PDF | DOI: 10.1371/journal.pone.0073577
Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, 23.1 plus\minus 1.52 years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia.
PDF | DOI: 10.1016/j.cmpb.2013.03.014
Noise is omnipresent in biomedical systems and signals. Conventional views assume that its presence is detrimental to systems' performance and accuracy. Hence, various analytic approaches and instrumentation have been designed to remove noise. On the contrary, recent contributions have shown that noise can play a beneficial role in biomedical systems. The results of this literature review indicate that noise is an essential part of biomedical systems and often plays a fundamental role in the performance of these systems. Furthermore, in preliminary work, noise has demonstrated therapeutic potential to alleviate the effects of various diseases. Further research into the role of noise and its applications in medicine is likely to lead to novel approaches to the treatment of diseases and prevention of disability.
PDF | DOI: 10.1109/TBME.2013.2243730
Swallowing accelerometry is a promising non-invasive approach for the detection of swallowing difficulties. In this paper, we propose an approach for classification of swallowing acceleroemtry recordings containing either healthy swallows or penetration-aspiration (entry of material into the airway) in dysphagic patients. The proposed algorithm is based on the wavelet packet decomposition of swallowing accelerometry signals in combination with linear discriminant analysis as a feature reduction method and Bayes classification. The proposed algorithm was tested using swallowing accelerometry signals collected from 40 patients during the regularly scheduled videoflouroscopy exam. The participants were instructed to swallow several five milliliter sips of thin liquid barium in a head neutral position. The results of our numerical analysis showed that the proposed algorithm can differentiate healthy swallows from aspiration swallows with an accuracy greater than 90\%. These results position swallowing accelerometry as a valid approach for the detection of swallowing difficulties, particularly penetration-aspiration in patients suspected of dysphagia.
PDF | DOI: 10.4236/ojvm.2013.31014
The overall goal of this project is to develop a humane non-human primate model of traumatic spinal cord injury that will facilitate the development and evaluation of therapeutic interventions. The model utilizes neurophysiological techniques to identify the precise location of the upper motor neuron axons that innervate the lower motor neurons that control tail musculature. This facilitates the placement of a selective lesion that partially disconnects the upper and lower motor neuron supply to the musculature of the tail. An implanted transmitter quantitatively measures electomyographic data from the tail. The preliminary data indicates that this model is feasible. The subject was able to tolerate the implantation of the transmitter, without adverse effects. As well, there was no limb impairment, bowel dysfunction or bladder dysfunction. The histopathologic and electromyographic features of the selective experimental lesion were similar to human spinal cord injury.
PDF | DOI: 10.1007/s00455-012-9418-9
Aspiration (the entry of foreign contents into the upper airway) is a serious concern for individuals with dysphagia and can lead to pneumonia. However, overt signs of aspiration, such as cough, are not always present, making non-instrumental diagnosis challenging. Valid, reliable tools for detecting aspiration during clinical screening and assessment are needed. In this study, we investigated the validity of a noninvasive accelerometry signal processing classifier for detecting aspiration. Dual-axis cervical accelerometry signals were collected from 40 adults on thin-liquid swallowing tasks during videofluoroscopic swallowing examinations. Signal processing algorithms were used to remove known sources of artifact and a classifier was trained to identify signals associated with penetration-aspiration. Validity was measured in comparison to blinded ratings of penetration-aspiration from the concurrently recorded videofluoroscopies. On a bolus-by-bolus basis, the accelerometry classifier had a 10% false negative rate (90% sensitivity) and a 23% false positive rate (77% specificity) for detecting penetrationaspiration. We conclude that accelerometry can be used to support valid, reliable and efficient detection of aspiration risk in patients with suspected dysphagia.
PDF | DOI: 10.1371/journal.pone.0055405
Functional transcrannial Doppler (fTCD) is used for monitoring the hemodynamics characteristics of major cerebral arteries. Its resting-state characteristics are known only when considering the maximal velocity corresponding to the highest Doppler shift (so called the envelope signals). Significantly more information about the resting-state fTCD can be gained when considering the raw cerebral blood flow velocity (CBFV) recordings. In this paper, we considered simultaneously acquired envelope and raw CBFV signals. Specifically, we collected bilateral CBFV recordings from left and right middle cerebral arteries using 20 healthy subjects (10 females). The data collection lasted for 15 minutes. The subjects were asked to remain awake, stay silent, and try to remain thought-free during the data collection. Time, frequency and time-frequency features were extracted from both the raw and the envelope CBFV signals. The effects of age, sex and body-mass index were examined on the extracted features. The results showed that the raw CBFV signals had a higher frequency content, and its temporal structures were almost uncorrelated. The information-theoretic features showed that the raw recordings from left and right middle cerebral arteries had higher content of mutual information than the envelope signals. Age and body-mass index did not have statistically significant effects on the extracted features. Sex-based differences were observed in all three domains and for both, the envelope signals and the raw CBFV signals. These findings indicate that the raw CBFV signals provide valuable information about the cerebral blood flow which can be utilized in further validation of fTCD as a clinical tool.
PDF | DOI: 10.1016/j.physd.2012.09.003
In this paper, a scaling exponent based approach is proposed to determine the state of chaotic circuits, and the scaling exponent is calculated using detrended fluctuation analysis (DFA). The corresponding detector is designed using the fact that the scaling exponent changes for various states of chaotic circuits. Simulation examples in this paper are performed for the Chua's circuit and other chaotic systems and compared with the state-of-the-art in the field. The proposed detector outperforms existing techniques in ability to distinguish the chaotic and periodic states in the circuits for relatively high noise.
PDF | DOI: 10.1016/j.neulet.2012.09.030
In this study, we conducted an offline analysis of transcranial Doppler (TCD) ultrasound recordings to investigate potential methods for increasing data transmission rate in a TCD-based brain-computer interface. Cerebral blood flow velocity was recorded within the left and right middle cerebral arteries while nine able-bodied participants alternated between rest and two different mental activities (word generation and mental rotation). We differentiated these three states using a three-class linear discriminant analysis classifier while the duration of each state was varied between 5 and 30 seconds. Maximum classification accuracies exceeded 70%, and data transmission rate was maximized at 1.2 bits per minute, representing a four-fold increase in data transmission rate over previous two-class analysis of TCD recordings.
PDF | DOI: 10.1109/TBME.2012.2205577
The purpose of this study is to utilize and demonstrate the use of the self-organizing map (SOM) method for visualization, modelling and comparison of trunk neuromuscular synergies during sitting. Thirteen participants were perturbed at the level of the sternum, in eight directions during sitting. Electromyographic (EMG) responses of ten trunk muscles involved in postural control were recorded. The SOM was used to encode the EMG responses on a two-dimensional (2-D) projection (i.e., visualization). The result contains similar patterns mapped close together on the plot therefore forming clusters of data. Such visualization of ten EMG responses following eight directional perturbations allows comparisons of direction-dependent postural synergies. Direction-dependent neuromuscular response models for each muscle was then constructed from the SOM visualization. The results demonstrate that SOM was able to encode complex neuromuscular responses and the visualization shows direction-dependent differences in the postural synergies. Moreover, each muscle was modelled using the SOM-based method and shows that all muscles, except one, produce a Gaussian fit for direction-dependent responses which is supported in the literature. Overall, SOM analysis offers a reverse engineering method for exploration and comparison of complex neuromuscular systems, which can describe postural synergies at a glance.
PDF | DOI: 10.1371/journal.pone.0043104
Walking is a complex, rhythmic task performed by the locomotor system. However, natural gait rhythms can be influenced by metronomic auditory stimuli, a phenomenon of particular interest in neurological rehabilitation. In this paper, we examined the effects of aural, visual and tactile rhythmic cues on the temporal dynamics associated with human gait. Data were collected from fifteen healthy adults in two sessions. Each session consisted of five 15-minute trials. In the first trial of each session, participants walked at their preferred walking speed. In subsequent trials, participants were asked to walk to a metronomic beat, provided through visually, aurally, tactile or all three cues (simultaneously and in sync), the pace of which was set to the preferred walking speed of the first trial. Using the collected data, we extracted several parameters including: gait speed, mean stride interval, stride interval variability, scaling exponent and maximum Lyapunov exponent. The extracted parameters showed that rhythmic sensory cues affect the temporal dynamics of human gait. The auditory rhythmic cue had the greatest influence on the gait parameters, while the visual cue had no statistically significant effect on the scaling exponent. These results demonstrate that visual rhythmic cues could be considered as an alternative cueing modality in rehabilitation without concern of adversely altering the statistical persistence of walking.
Background: Dysphagia or swallowing disorder negatively impacts a child's health and development. The gold standard of dysphagia detection is videofluoroscopy which exposes the child to ionizing radiation, and requires specialized clinical expertise and expensive institutionally-based equipment, precluding day-to-day and repeated assessment of fluctuating swallowing function. Swallowing accelerometry is the non-invasive measurement of cervical vibrations during swallowing and may provide a portable and cost-efective bedside alternative. In particular, dual-axis swallowing accelerometry has demonstrated screening potential in older persons with neurogenic dysphagia, but the technique has not been evaluated in the pediatric population. Methods: In this study, dual-axis accelerometric signals were collected simultaneous to video fluoroscopic records from 29 pediatric participants (age 6.8 plus/minus 4.8 years; 20 males) previously diagnosed with neurogenic dysphagia. Participants swallowed 3-5 sips of barium-coated boluses of different consistencies (normally, from thick puree to thin liquid) by spoon or bottle. Videofluoroscopic records were reviewed retrospectively by a clinical expert to extract swallow timings and ratings. The dual-axis acceleration signals corresponding to each identified swallow were pre-processed, segmented and trimmed prior to feature extraction from time, frequency, time-frequency and information theoretic domains. Feature space dimensionality was reduced via principal components. Results: Using 8-fold cross-validation, 16-17 dimensions and a support vector machine classifier with an RBF kernel, an adjusted accuracy of 89.6% plus\minus 0.9 was achieved for the discrimination between swallows with and with out airway entry. Conclusions: Our results suggest that dual-axis accelerometry has merit in the non-invasive detection of unsafe swallows in children and deserves further consideration as a pediatric medical device.
PDF | DOI: 10.1016/j.humov.2011.05.007
In recent years, there has been considerable interest in the effects of auditory and visual distractions on pedestrian ambulation. A fundamental temporal characteristic of ambulation is the temporal fluctuation of the stride interval. In this paper, we investigate the stationarity of stride interval time series when people are exposed to different forms of auditory and visual distractions. An increase in nonstationary behavior may be suggestive of divided attention and more frequent central modulation of locomotion, both of which may have ramifications on pedestrian vigilance and responsiveness to environmental perturbations. One group of fifteen able-bodied (6 females) young adult participants completed a music protocol (overground walking with and without music). A second group of fifteen (7 females) did a television protocol (treadmill walking while watching TV with and without sound). Three walking trials, each 15 minutes in duration, were performed at each participant's comfortable walking speed, with force sensitive resistors under the heel of each foot. Using the reverse arrangements test, the vast majority of time series were nonstationary, with a time-varying mean as the principal source of nonstationarity. Furthermore, the television trial with sound had the greatest number of nonstationarities followed by overground walking while listening to music. We discuss the possibility that these conditions measurably affect gait dynamics through a subconscious synchronization to external rhythms or a cyclic distraction followed by a period of increased conscious correction of gait timing. Our findings suggest that the regulation of stride timing is particularly susceptible to constant, time-evolving auditory stimuli, but that normal pacing can be restored quickly upon stimulus withdrawal. These kinds of sensory distractions should thus be carefully considered in studies of pedestrian ambulation.
PDF | DOI: 10.1186/1687-6180-2012-101
Monitoring physiological functions such as swallowing often generates large volumes of samples to be stored and processed, which can introduce computational constraints especially if remote monitoring is desired. In this paper, we propose a compressive sensing (CS) algorithm to alleviate some of these issues while acquiring dual-axis swallowing accelerometry signals. The proposed CS approach uses a time-frequency dictionary where the members are modulated discrete prolate spheroidal sequences (MDPSS). These waveforms are obtained by modulation and variation of discrete prolate spheroidal sequences (DPSS) in order to reflect the time-varying nature of swallowing acclerometry signals. While the modulated bases permit one to represent the signal behavior accurately, the matching pursuit algorithm is adopted to iteratively decompose the signals into an expansion of the dictionary bases. To test the accuracy of the proposed scheme, we carried out several numerical experiments with synthetic test signals and dual-axis swallowing accelerometry signals. In both cases, the proposed CS approach based on the MDPSS yields more accurate representations than the CS approach based on DPSS. Specifically, we show that dual-axis swallowing accelerometry signals can be accurately reconstructed even when the sampling rate is reduced to half of the Nyquist rate. The results clearly indicate that the MDPSS are suitable bases for swallowing accelerometry signals.
PDF | DOI: 10.1371/journal.pone.0033464
Head movements can greatly affect swallowing accelerometry signals. In this paper, we implement a spline-based approach to remove low frequency components associated with these motions. Our approach was tested using both synthetic and real data. Synthetic signals were used to perform a comparative analysis of the spline-based approach with other similar techniques. Real data, obtained data from 408 healthy participants during various swallowing tasks, was used to analyze the processing accuracy with and without the spline-based head motions removal scheme. Specifically, we analyzed the segmentation accuracy and the effects of the scheme on statistical properties of these signals, as measured by the scaling analysis. The results of the numerical analysis showed that the spline-based technique achieves a superior performance in comparison to other existing techniques. Additionally, when applied to real data, we improved the accuracy of the segmentation process by achieving a 27 \% drop in the number of false negatives and a 30 \% drop in the number of false positives. Furthermore, the anthropometric trends in the statistical properties of these signals remained unaltered as shown by the scaling analysis, but the strength of statistical persistence was significantly reduced. These results clearly indicate that any future medical devices based on swallowing accelerometry signals should remove head motions from these signals in order to increase segmentation accuracy.
PDF | DOI: 10.1186/1475-925X-10-100
Background: Swallowing accelerometry has been suggested as a potential non-invasive tool for bedside dysphagia screening. Various vibratory signal features and complementary measurement modalities have been put forth in the literature for the potential discrimination between safe and unsafe swallowing. To date, automatic classification of swallowing accelerometry has exclusively involved a single-axis of vibration although a second axis is known to contain additional information about the nature of the swallow. Furthermore, the only published attempt at automatic classification in adult patients has been based on a small sample of swallowing vibrations. Methods: In this paper, a large corpus of dual-axis accelerometric signals were collected from 30 older adults (aged 65.47 plus\minus 13.4 years, 15 male) referred to videofluoroscopic examination on the suspicion of dysphagia. We invoked a reputation-based classifier combination to automatically categorize the dual-axis accelerometric signals into safe and unsafe swallows, as labeled via videofluoroscopic review. From these participants, a total of 224 swallowing samples were obtained, 164 of which were labeled as unsafe swallows (swallows where the bolus entered the airway) and 60 as safe swallows. Three separate support vector machine (SVM) classifiers and eight different features were selected for classification. Results: With selected time, frequency and information theoretic features, the reputation-based algorithm distinguished between safe and unsafe swallowing with promising accuracy (80.48 plus\minus 5.0%), high sensitivity (97.1 plus\minus 2%) and modest specificity (64 plus\minus 8.8%). Interpretation of the most discriminatory features revealed that in general, unsafe swallows had lower mean vibration amplitude and faster autocorrelation decay, suggestive of decreased hyoid excursion and compromised coordination, respectively. Further, owing to its performance-based weighting of component classifiers, the static reputation-based algorithm outperformed the democratic majority voting algorithm on this clinical data set. Conclusions: Given its computational efficiency and high sensitivity, reputation-based classification of dual-axis accelerometry ought to be considered in future developments of a point-of-care swallow assessment where clinical informatics are desired.
PDF | DOI: 10.2337/dc11-0969
Objective: To investigate the effects of inflammation on perfusion regulation and brain volumes in type 2 diabetes. Methods: 147 subjects (71 diabetic, 76 non-diabetic, aged 65.2±8yrs) were studied using 3T anatomical and continuous arterial spin labeling MRI. We analyzed the relationship between serum soluble vascular and intercellular adhesion molecules (sVCAM, sICAM - markers of endothelial integrity), regional vasoreactivity and tissue volumes. Results: Diabetic subjects had greater vasoconstriction reactivity, more atrophy, depression and slower walking. Adhesion molecules were specifically related to gray matter atrophy (p=0.04) and altered vasoreactivity (p=0.03) in the diabetes and control groups. Regionally, sVCAM and sICAM were linked to exaggerated vasoconstriction, blunted vasodilatation and increased cortical atrophy in the frontal, temporal and parietal lobes (p=0.04-0.003). sICAM correlated with worse functionality. Conclusions: Diabetes is associated with cortical atrophy, vasoconstriction and worse performance. Adhesion molecules, as markers of vascular health, have been indicated to contribute to altered vasoregulation and atrophy.
PDF | DOI: 10.1371/journal.pone.0024170
In this study, we investigate the feasibility of a BCI based on transcranial Doppler ultrasound (TCD), a medical imaging technique used to monitor cerebral blood floow velocity. We classified the cerebral blood flow velocity changes associated with two mental tasks - a word generation task, and a mental rotation task. Cerebral blood flow velocity was measured simultaneously within the left and right middle cerebral arteries while nine able-bodied adults alternated between mental activity (i.e. word generation and mental rotation) and relaxation. Using linear discriminant analysis and a set of time domain features, word generation and mental rotation were classified with respective average accuracies of 82.9% plus/minus 10.5 and 85.7% plus/minus 10.0 across all participants. Accuracies for all participants significantly exceeded chance. These results indicate that TCD is a promising measurement modality for BCI research.
PDF | DOI: 10.1016/j.cmpb.2010.06.010
Dual-axis cervical accelerometry is an emerging approach for the assessment of swallowing difficulties. However, the baseline signals, i.e., vibration signals with only quiet breathing or apnea but without swallowing, are not well understood. In particular, to comprehend the contaminant effects of head motion on cervical accelerometry, we need to study the scaling behavior of these baseline signals. Dual-axis accelerometry data were collected from 50 healthy adult participants under conditions of quiet breathing, apnea and selected head motions, all in the absence of swallowing. The denoised cervical vibrations were subjected to detrended fluctuation analysis with empirically determined first-order detrending. Strong persistence was identified in cervical vibration signals in both anterior–posterior (A–P) and superior–inferior (S–I) directions, under all the above experimental conditions. Vibrations in the A–P axes exhibited stronger correlations than those in the S–I axes, possibly as a result of axis-specific effects of vasomotion. In both axes, stronger correlations were found in the presence of head motion than without, suggesting that head movement significantly impacts baseline cervical accelerometry. No gender or age effects were found on statistical persistence of either vibration axes. Future developments of cervical accelerometry-based medical devices should actively mitigate the effects of head movement.
PDF | DOI: 10.1016/j.sigpro.2010.10.008
Fractional Fourier transform (FRFT) is a generalization of the Fourier transform, rediscovered many times over the past 100 years. In this paper, we provide an overview of recent contributions pertaining to the FRFT. Specifically, the paper is geared toward signal processing practitioners by emphasizing the practical digital realizations and applications of the FRFT. It discusses three major topics. First, the manuscripts relates the FRFT to other mathematical transforms. Second, it discusses various approaches for practical realizations of the FRFT. Third, we overview the practical applications of the FRFT. From these discussions, we can clearly state that the FRFT is closely related to other mathematical transforms, such as time–frequency and linear canonical transforms. Nevertheless, we still feel that major contributions are expected in the field of the digital realizations and its applications, especially, since many digital realizations of the FRFT still lack properties of the continuous FRFT. Overall, the FRFT is a valuable signal processing tool. Its practical applications are expected to grow significantly in years to come, given that the FRFT offers many advantages over the traditional Fourier analysis.
PDF | DOI: 10.1109/LSP.2010.2097590
We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds on the MSE. The MSE bounds can be used to evaluate the denoising method. Our simulation results confirm that not only does the method provide an accurate estimate of the MSE for any given thresholding method, but the proposed method can also search and find an optimum threshold for any noisy data with regard to MSE.
PDF | DOI: 10.1159/000319737
Objective: The chin-down maneuver is commonly used in dysphagia management to facilitate greater airway protection. However, the literature suggests that variation in maneuver execution may threaten the effectiveness of the intervention. Our goal was to study variation in chin-down maneuver execution given a uniform instruction. Methods: Sagittal view digital video recordings were acquired from 408 healthy adults who performed sequences of reiterated water swallows in head-neutral and chin-down positions. Head angle measurements were extracted from the recordings, using markers on goggles worn by 176 participants. Results: We observed considerable variation in head angle in the head-neutral swallowing task, with a trend to greater flexion in participants over the age of 65. Male participants showed greater variation in head angle than females. Head flexion during the chin-down swallowing tasks averaged 19°, in the range reported to yield clinical benefit in radiographic studies. Conclusion: We conclude that a clear, uniform instruction is adequate to facilitate execution of the chin-down maneuver to a degree that is likely to be of clinical benefit. The variation in head angle observed in this study warrants further research, particularly regarding the relationship between anatomical cervical spine curvature and head angle influence on swallowing.
PDF | DOI: 10.1016/j.humov.2010.07.015
Treadmills are commonly implemented in rehabilitation and laboratory settings to facilitate gait analysis and training. However, while this locomotor modality is often used with children, its effect on pediatric stride interval dynamics is unknown. This study investigated the stride interval persistence of 30 asymptomatic children after completion of three to six 10-min walking trials comprised of: (i) overground walking (OW), (ii) unsupported treadmill walking (UTW), and (iii) handrail-supported treadmill walking (STW). The primary outcome measure was alpha, a quantifier of stride interval persistence obtained from detrended fluctuation analysis. Preferred walking speed, number of strides taken, stride interval duration, and stride interval coefficient of variation were also assessed. Stride interval persistence was significantly diminished during both treadmill walking conditions, compared to overground walking, with the largest decrease in alpha during UTW. Preferred speed, number of strides, and stride interval duration also differed between overground and treadmill walking, and older children demonstrated reduced stride interval variability compared to younger children. The observed treadmill and age effects on stride parameters may be due to a combination of differing locomotor constraints between overground and treadmill walking and developmental differences in sensory processing, cerebellar plasticity, and corticospinal involvement in locomotion.
PDF | DOI: 10.1016/j.humov.2010.04.009
Numerous measures of dynamic stability have been proposed to gauge fall risk in the elderly, including stride interval variability and variability of the center of mass. However, these measures have been deemed inadequate because they do not take into account temporal information. Therefore, research on the measurement of dynamic stability has turned to other analysis methods such as stride interval dynamics and the maximum Lyapunov exponent. Stride interval dynamics reflect the statistical persistence of an individual’s stride interval time series and the Lyapunov exponent quantifies local dynamic stability – the sensitivity of the system to infinitesimal perturbations. In this study, we compare the ability of these measurement tools to detect changes between overground and compliant-surface walking, a condition known to affect stability, to determine their aptness as measures of dynamic stability. Fourteen able-bodied participants completed three 15 min walks, two overground and one on a compliant surface. Our results show that the Lyapunov exponent may be more sensitive to gait changes than stride interval dynamics and gait variability measures.
PDF | DOI: 10.1016/j.compbiomed.2010.09.002
Swallowing accelerometry is a biomechanical approach for the assessment of difficulties during deglutition. However, the effects of various swallowing tasks and different anthropometric/demographic variables on the statistical behavior of these accelerometric signals are unknown. In particular, to understand the statistical persistence of these signals, we used detrended fluctuation analysis (DFA) to analyze accelerometric data collected from 408 healthy participants during dry, wet and wet chin tuck swallowing tasks. The results of DFA were then examined for potential influences of age, gender or body mass index. Several important conclusions were reached. First, the strongest persistence was observed for the wet chin tuck swallows. Second, the vibrations in the superior–inferior (S–I) direction generally have stronger temporal dependencies than those in the anterior–posterior (A–P) direction. Both of these phenomena can be attributed to the dominating influence of head movements on the amplitude of vibrations in the S–I direction. Third, gender, age and body mass index of the participants did not impact the observed persistence for dry and wet chin tuck swallows, while a gender effect was identified for wet swallows. In particular, male participants experienced more Brownian-like statistical dependencies in their swallowing signals. Future developments in the field should attempt to remove signal components associated with strong statistical persistence, as they tend to be associated with non-swallowing phenomena.
PDF | DOI: 10.1186/1756-0500-3-269
Background: Head motions can severely affect dual-axis cervical acceloremetry signals. A complete understanding of the effects of head motion is required before a robust accelerometry-based medical device can be developed. In this paper, we examine the spectral characteristics of dual-axis cervical accelerometry signals in the absence of swallowing but in the presence of head motions. Findings: Data from 50 healthy adults were collected while participants performed five different head motions. Three different spectral features were extracted from each recording: peak frequency, spectral centroid and bandwidth. Statistical analyses showed that peak frequencies are independent of the type of head motion, participant gender and age. However, spectral centroids are statistically different between the anterior-posterior (A-P) and superior-inferior (S-I) directions and between different motion. Additionally, statistically different bandwidths are observed for head tilts down and back between the A-P and the S-I directions. Conclusions: These differences indicate that head motions induce additional non-dominant spectral components in dual-axis cervical recordings. The results presented here suggest that head motion ought to be considered in the development of medical devices based on dual-axis cervical accelerometery signals.
PDF | DOI: 10.1016/j.medengphy.2010.04.008
Automatic segmentation of dual-axis swallowing accelerometry signals can be severely affected by strong vocalizations. In this paper, a method based on periodicity detection is proposed to detect and remove such vocalizations. Periodic signal components are detected using conventional speech processing techniques and information from both axes are combined to improve vocalization detection accuracy. Experiments with 408 healthy subjects performing dry, wet, and wet chin tuck swallows show that the proposed method attains an average 95.3% sensitivity and 96.3% specificity. When applied in conjunction with an automatic segmentation algorithm, it is observed that segmentation accuracy improves by approximately 55%. These results encourage further development of medical devices for the detection of swallowing difficulties.
PDF | DOI: 10.1186/1475-925X-9-23
Background: Recently, pattern recognition methods have been deployed in the classification of multiple activation states from mechanomyogram (MMG) signals for the purpose of controlling switching interfaces. Given the propagative properties of MMG signals, it has been suggested that MMG classification should be robust to changes in sensor placement. Nonetheless, this purported robustness remains speculative to date. This study sought to quantify the change in classification accuracy, if any, when a classifier trained with MMG signals from the muscle belly, is subsequently tested with MMG signals from a nearby location. Methods: An arrangement of 5 accelerometers was attached to the flexor carpi radialis muscle of 12 able-bodied participants; a reference accelerometer was located over the muscle belly, two peripheral accelerometers were positioned along the muscle's transverse axis and two more were aligned to the muscle's longitudinal axis. Participants performed three classes of muscle activity: wrist flexion, wrist extension and semi-pronation. A collection of time, frequency and time-frequency features were considered and reduced by genetic feature selection. The classifier, trained using features from the reference accelerometer, was tested with signals from the longitudinally and transversally displaced accelerometers. Results: Classification degradation due to accelerometer displacement was significant for all participants, and showed no consistent trend with the direction of displacement. Further, the displaced accelerometer signals showed task-dependent de-correlations with respect to the reference accelerometer. Conclusions: These results indicate that MMG signal features vary with spatial location and that accelerometer displacements of only 1-2 cm cause sufficient feature drift to significantly diminish classification accuracy. This finding emphasizes the importance of consistent sensor placement between MMG classifier training and deployment for accurate control of switching interfaces.
PDF | DOI: 10.1155/2010/323125
Fast Hermite projections have been often used in image-processing procedures such as image database retrieval, projection filtering, and texture analysis. In this paper, we propose an innovative approach for the analysis of one-dimensional biomedical signals that combines the Hermite projection method with time-frequency analysis. In particular, we propose a two-step approach to characterize vibrations of various origins in swallowing accelerometry signals. First, by using time-frequency analysis we obtain the energy distribution of signal frequency content in time. Second, by using fast Hermite projections we characterize whether the analyzed time-frequency regions are associated with swallowing or other phenomena (vocalization, noise, bursts, etc.). The numerical analysis of the proposed scheme clearly shows that by using a few Hermite functions, vibrations of various origins are distinguishable. These results will be the basis for further analysis of swallowing accelerometry to detect swallowing difficulties.
PDF | DOI: 10.1109/TSP.2010.2043972
Swallow accelerometry is an emerging tool for noninvasive dysphagia screening. However, the automatic detection of a swallowing event is challenging due to contaminant vibrations arising from head motion, speech and coughing. In this paper, we consider the acceleration signal as a stochastic diffusion where movement is associated with drift and swallowing with volatility. Using this model, we develop a volatility-based swallow event detector that operates on the raw acceleration signal in an online fashion. With data from healthy participants and patients with dysphagia, the proposed detector achieves performance comparable to previously proposed swallow segmentation algorithms, with the added benefit of online detection and no signal pre-processing. The volatility-based detector may be useful for event identification in other biomechanical applications that rely on accelerometry signals.
PDF | DOI: 10.1007/s10439-009-9874-z
Dual-axis swallowing accelerometry is a promising noninvasive tool for the assessment of difficulties during deglutition. The resting and anaerobic characteristics of these signals, however, are still unknown. This paper presents a study of baseline characteristics (stationarity, spectral features, and information content) of dual-axis cervical vibrations. In addition, modeling of a data acquisition system was performed to annul any undesired instrumentation effects. Two independent data collection procedures were conducted to fulfil the goals of the study. For baseline characterization, data were acquired from 50 healthy adult subjects. To model the data acquisition (DAQ) system, ten recordings were obtained while the system was exposed to random small vibrations. The inverse filtering approach removed extraneous effects introduced by the DAQ system. Approximately half of the filtered signals were stationary in nature. All signals exhibited a level of statistical dependence between the two axes. Also, there were very low frequency oscillations present in these signals that may be attributable to vasomotion of blood vessels near the thyroid cartilage, blood flow, and respiration. Demographic variables such as age and gender did not statistically influence baseline information-theoretic signal characteristics. However, participant age did affect the baseline spectral characteristics. These findings are important to the further development of diagnostic devices based on dual-axis swallowing vibration signals.
PDF | DOI: 10.1016/j.gaitpost.2009.12.002
Stride interval series exhibit statistical persistence, and detrended fluctuation analysis (DFA) is a routinely employed technique for describing this behavior. However, the implementation of DFA to gait data varies considerably between studies. We empirically examine two practical aspects of DFA which significantly affect the analysis outcome: the box size range and the stride interval series length. We conduct an analysis of their effect using stride intervals from 16 able-bodied adults, for overground walking, treadmill walking while holding a handrail, and treadmill walking without using a handrail. Our goal is to provide general guidelines for these two choices, with the aim of standardizing the application of DFA and facilitating inter-study comparisons. Based on the results of our analysis, we propose the use of box sizes from 16 to N/9, where N is the number of stride intervals. Moreover, for differentiating between normal and pathological walking with reasonable accuracy, we recommend a minimum of 600 stride intervals.
PDF | DOI: 10.1186/1475-925X-9-11
Background: Electrodermal reactions (EDRs) can be attributed to many origins, including spontaneous fluctuations of electrodermal activity (EDA) and stimuli such as deep inspirations, voluntary mental activity and startling events. In fields that use EDA as a measure of psychophysiological state, the fact that EDRs may be elicited from many different stimuli is often ignored. This study attempts to classify observed EDRs as voluntary (i.e., generated from intentional respiratory or mental activity) or involuntary (i.e., generated from startling events or spontaneous electrodermal fluctuations). Methods: Eight able-bodied participants were subjected to conditions that would cause a change in EDA: music imagery, startling noises, and deep inspirations. A user-centered cardiorespiratory classifier consisting of 1) an EDR detector, 2) a respiratory filter and 3) a cardiorespiratory filter was developed to automatically detect a participant's EDRs and to classify the origin of their stimulation as voluntary or involuntary. Results: Detected EDRs were classified with a positive predictive value of 78%, a negative predictive value of 81% and an overall accuracy of 78%. Without the classifier, EDRs could only be correctly attributed as voluntary or involuntary with an accuracy of 50%. Conclusions: The proposed classifier may enable investigators to form more accurate interpretations of electrodermal activity as a measure of an individual's psychophysiological state.
PDF | DOI: 10.1186/1475-925X-9-7
Background: Dual-axis swallowing accelerometry has recently been proposed as a tool for non-invasive analysis of swallowing function. Although swallowing is known to be physiologically modifiable by the type of food or liquid (i.e., stimuli), the effects of stimuli on dual-axis accelerometry signals have never been thoroughly investigated. Thus, the objective of this study was to investigate stimulus effects on dual-axis accelerometry signal characteristics. Signals were acquired from 17 healthy participants while swallowing 4 different stimuli: water, nectar-thick and honey-thick apple juices, and a thin-liquid barium suspension. Two swallowing tasks were examined: discrete and sequential. A variety of features were extracted in the time and time-frequency domains after swallow segmentation and pre-processing. A separate Friedman test was conducted for each feature and for each swallowing task. Results: Significant main stimulus effects were found on 6 out of 30 features for the discrete task and on 5 out of 30 features for the sequential task. Analysis of the features with significant stimulus effects suggested that the changes in the signals revealed slower and more pronounced swallowing patterns with increasing bolus viscosity. Conclusions: We conclude that stimulus type does affect specific characteristics of dual-axis swallowing accelerometry signals, suggesting that associated clinical screening protocols may need to be stimulus specific.
PDF | DOI: 10.1186/1756-0500-3-47
Background: Stride interval persistence, a term used to describe the correlation structure of stride interval time series, is thought to provide insight into neuromotor control, though its exact clinical meaning has not yet been realized. Since human locomotion is shaped by energy efficient movements, it has been hypothesized that stride interval dynamics and energy expenditure may be inherently tied, both having demonstrated similar sensitivities to age, disease, and pace-constrained walking. Findings: This study tested for correlations between stride interval persistence and measures of energy expenditure including mass-specific gross oxygen consumption per minute, mass-specific gross oxygen cost per meter (VO2) and heart rate (HR). Metabolic and stride interval data were collected from 30 asymptomatic children who completed one 10-minute walking trial under each of the following conditions: (i) overground walking, (ii) hands-free treadmill walking, and (iii) handrail-supported treadmill walking. Stride interval persistence was not significantly correlated with (p > 0.32), VO2 (p > 0.18) or HR (p > 0.56). Conclusions: No simple linear dependence exists between stride interval persistence and measures of gross energy expenditure in asymptomatic children when walking overground and on a treadmill.
PDF | DOI: 10.1049/el.2010.2334
Time-frequency representations of signals obtained by the S-transform can be very sensitive to the presence of alpha-stable noise. An algorithm for the robust S-transform is introduced. The proposed scheme is based on the L-DFT. The results of conducted numerical analysis show a significantly enhanced performance of the proposed scheme compared to the standard S-transform.
PDF | DOI: 10.1016/j.humov.2009.09.002
Fluctuations in the stride interval of human gait have been found to exhibit statistical persistence over hundreds of strides, the extent of which changes with age, pathology, and speed-constrained walking. Thus, recent investigations have focused on quantifying this scaling behavior in order to gain insight into locomotor control. While the ability of a given analysis technique to provide an accurate scaling estimate depends largely on the stationary properties of the given series, direct investigation of stride interval stationarity has been largely overlooked. In the present study we test the stride interval time series obtained from able-bodied children for weak stationarity. Specifically, we analyze signals obtained during three distinct modes of self-paced locomotion: (i) overground walking, (ii) unsupported (hands-free) treadmill walking, and (iii) handrail-supported treadmill walking. Using the reverse arrangements test, we identify non-stationary signals in all three walking conditions and find the major known cause to be due to time-varying first and second moments. We further discuss our findings in terms of locomotor control and the differences between the locomotor modalities investigated. Overall, our results advocate against scaling analysis techniques that assume stationarity.
PDF | DOI: 10.1088/0967-3334/31/1/N01
Dual-axis swallowing accelerometry is an emerging tool for the assessment of dysphagia (swallowing difficulties). These signals however can be very noisy as a result of physiological and motion artifacts. In this note, we propose a novel scheme for denoising those signals, i.e. a computationally efficient search for the optimal denoising threshold within a reduced wavelet subspace. To determine a viable subspace, the algorithm relies on the minimum value of the estimated upper bound for the reconstruction error. A numerical analysis of the proposed scheme using synthetic test signals demonstrated that the proposed scheme is computationally more efficient than minimum noiseless description length (MNDL)-based denoising. It also yields smaller reconstruction errors than MNDL, SURE and Donoho denoising methods. When applied to dual-axis swallowing accelerometry signals, the proposed scheme exhibits improved performance for dry, wet and wet chin tuck swallows. These results are important for the further development of medical devices based on dual-axis swallowing accelerometry signals.
PDF | DOI: 10.1186/1475-925X-8-25
Background: A common but debated technique in the management of swallowing difficulties is the chin tuck swallow, where the neck is flexed forward prior to swallowing. Natural variations in chin tuck angles across individuals may contribute to the differential effectiveness of the technique. Methodology: To facilitate the study of chin tuck angle variations, we present a template tracking algorithm that automatically extracts neck angles from sagittal videos of individuals performing chin tuck swallows. Three yellow markers geometrically arranged on a pair of dark visors were used as tracking cues. Results: The algorithm was applied to data collected from 178 healthy participants during neutral and chin tuck position swallows. Our analyses revealed no major influences of body mass index and age on neck flexion angles during swallowing, while gender influenced the average neck angle only during wet swallows in the neutral position. Chin tuck angles seem to be independent of anthropometry and gender in healthy adults, but deserve further study in pathological populations. Conclusion: The proposed neck flexion angle extraction algorithm may be useful in future studies where strict participant compliance to swallowing task protocol can be assured.
PDF | DOI: 10.1109/TBME.2008.2010504
Dysphagia (swallowing difficulty) is a serious and debilitating condition that often accompanies stroke, acquired brain injury, and neurodegenerative illnesses. Individuals with dysphagia are prone to aspiration (the entry of foreign material into the airway), which directly increases the risk of serious respiratory consequences such as pneumonia. Swallowing accelerometry is a promising noninvasive tool for the detection of aspiration and the evaluation of swallowing. In this paper, dual-axis accelerometry was implemented since the motion of the hyolaryngeal complex occurs in both anterior-posterior and superior-inferior directions during swallowing. Dual-axis cervical accelerometry signals were acquired from 408 healthy subjects during dry, wet, and wet chin tuck swallowing tasks. The proposed segmentation algorithm is based on the idea of sequential fuzzy partitioning of the signal and is well suited for long signals with nonstationary variance. The algorithm was validated with simulated signals with known swallowing locations and a subset of 295 real swallows manually segmented by an experienced speech language pathologist. In both cases, the algorithm extracted individual swallows with over 90% accuracy. The time duration analysis was carried out with respect to gender, body mass index (BMI), and age. Demographic and anthropometric variables influenced the duration of these segmented signals. Male participants exhibited longer swallows than female participants (p=0.05). Older participants and participants with higher BMIs exhibited swallows with significantly longer (p=0.05) duration than younger participants and those with lower BMIs, respectively.
PDF | DOI: 10.1016/j.dsp.2007.12.004
Signal processing can be found in many applications and its primary goal is to provide underlying information on specific problems for the purpose of decision making. Traditional signal processing approaches assume the stationarity of signals, which in practice is not often satisfied. Hence, time or frequency descriptions alone are insufficient to provide comprehensive information about such signals. On the contrary, time–frequency analysis is more suitable for nonstationary signals. Therefore, this paper provides a status report of feature based signal processing in the time–frequency domain through an overview of recent contributions. The feature considered here is energy concentration. The paper provides an analysis of several classes of feature extractors, i.e., time–frequency representations, and feature classifiers. The results of the literature review indicate that time–frequency domain signal processing using energy concentration as a feature is a very powerful tool and has been utilized in numerous applications. The expectation is that further research and applications of these algorithms will flourish in the near future.
PDF | DOI: 10.1109/TSP.2008.924856
Instantaneous frequency (IF) estimation through the estimation of peak locations in the time-frequency plane is an important approach for signals contaminated with additive white Gaussian noise. In this paper, the forementioned analysis is carried out for continuous wavelet transform. The analysis of the scalogram as the instantaneous frequency estimator is performed for any FM signal regardless of the mother wavelet. Accurate expressions for the bias and the variance of the estimator are derived, and reveal that the bias and the variance are signal dependent. Results are statistically confirmed through the numerical analysis for several mother wavelets, and among considered wavelets, the Morlet wavelet produces the smallest estimation error. Furthermore, the performance of the IF estimator based on the scalogram and the spectrogram were compared through analysis of mean square error. These results showed that the scalogram with the Morlet wavelet exhibited good performance for the sample linear FM signal and the sample hyperbolic FM signal in comparison to the spectrogram.
PDF | DOI: 10.1109/LSP.2008.917014
Instantaneous frequency (IF) is a fundamental concept that can be found in many disciplines such as communications, speech, and music processing. In this letter, analysis of an IF estimator, based on a time-frequency technique known as S-transform, is performed. The performance analysis is carried out in a white Gaussian noise environment, and expressions for the bias and the variance of the estimator are determined. The results show that the bias and the variance are signal dependent. This has been statistically confirmed through numerical simulations of several signal classes.
PDF | DOI: 10.1016/j.aeue.2007.03.014
The S-transform combines properties of the short-time Fourier (STFT) and wavelet transforms. It preserves the phase information of a signal as in the STFT, while providing the variable resolution as in the wavelet transform. However, the S-transform suffers from poor energy concentration for some classes of signals. A modification to the existing S-transform is proposed in this paper to enhance the energy concentration in the time–frequency domain. An improvement is achieved by introducing an additional parameter which can be used to optimize the window width. The optimization is performed over frequency and the proposed modification keeps the frequency marginal of the S-transform. The proposed scheme is tested on a set of synthetic signals. The results show that the proposed algorithm produces enhanced energy concentration in comparison to the standard S-transform. Also, the results show that for various signal types the proposed algorithm achieves higher signal concentration in comparison to other standard time–frequency transforms, such as, STFT and pseudo Wigner–Ville distribution (PWVD). Furthermore, it is concluded by numerical study that the proposed algorithm provides more accurate estimation of the instantaneous frequency than the standard S-transform.
PDF | DOI: 10.1155/2008/672941
Energy concentration of the S-transform in the time-frequency domain has been addressed in this paper by optimizing the width of the window function used. A new scheme is developed and referred to as a window width optimized S-transform. Two optimization schemes have been proposed, one for a constant window width, the other for time-varying window width. The former is intended for signals with constant or slowly varying frequencies, while the latter can deal with signals with fast changing frequency components. The proposed scheme has been evaluated using a set of test signals. The results have indicated that the new scheme can provide much improved energy concentration in the time-frequency domain in comparison with the standard S-transform. It is also shown using the test signals that the proposed scheme can lead to higher energy concentration in comparison with other standard linear techniques, such as short-time Fourier transform and its adaptive forms. Finally, the method has been demonstrated on engine knock signal analysis to show its effectiveness
PDF | DOI: 10.1109/TSMCA.2006.886333
In this paper, a novel correlation-based pattern classifier that relies on the analysis of time-frequency decomposition of a template and signals is proposed. Significant improvements in resolution and accuracy are obtained using this new classifier when compared to a conventional correlation-based one. The short-time Fourier transform, continuous wavelet transform, and S-transform are considered in the time-frequency decomposition process. To evaluate the performance of the proposed scheme, numerical studies are performed on a set of synthetic test signals, and excellent results have been obtained. This paper also presents an illustrative example where two types of heart sounds are classified. The classification error percentage for the heart sounds using the new classifier is only 6.670% as compared to 56.67% when a general correlation-based classifier is used.
PDF | DOI: 10.1016/j.ymssp.2005.01.010
This paper investigates the detection and diagnosis of brush seizing faults in the spindle positioning servo drive of a high-precision machining centre using a recently developed time–frequency pattern classification technique known as selective regional correlation (SRC). It is shown that SRC is capable of significantly enhancing the resolution of fault diagnosis when compared to conventional correlation-based techniques. The performance of this approach is evaluated using three time–frequency transformation techniques: the short-time Fourier transform (STFT), continuous wavelet transform (CWT) and S-Transform. In addition, three different 2D windows are used to isolate features for use with SRC: a rectangular (boxcar) window, a Gaussian window and a Kaiser window. The results have indicated that SRC is a promising tool for machine condition monitoring (MCM).