Showing papers by "Ervin Sejdic published in 2021"

New horizons in falls prevention and management for older adults: a global initiative

Abstract: 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.

Upper Esophageal Sphincter Opening Segmentation With Convolutional Recurrent Neural Networks in High Resolution Cervical Auscultation

Abstract: 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.

Tracking Hyoid Bone Displacement During Swallowing Without Videofluoroscopy Using Machine Learning of Vibratory Signals

Abstract: 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.

How Closely do Machine Ratings of Duration of UES Opening During Videofluoroscopy Approximate Clinician Ratings Using Temporal Kinematic Analyses and the MBSImP

Abstract: 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.

Acceleration Gait Measures as Proxies for Motor Skill of Walking: A Narrative Review

Abstract: 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.

Breath Acetone Sensing Based on Single-Walled Carbon Nanotube-Titanium Dioxide Hybrids Enabled by a Custom-Built Dehumidifier.

Abstract: 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.

A Preliminary Investigation of Whether HRCA Signals Can Differentiate Between Swallows from Healthy People and Swallows from People with Neurodegenerative Diseases.

Abstract: 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 < 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.

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department

Abstract: 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. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.

A generalized equation approach for hyoid bone displacement and penetration–aspiration scale analysis

Abstract: 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.

On the robustness of high-resolution cervical auscultation-based detection of upper esophageal sphincter opening duration in diverse populations

Abstract: Swallowing dysfunction, or dysphagia, occurs secondary to many underlying etiologies such as stroke and can lead to pneumonia. The upper esophageal sphincter (UES) is a major anatomical landmark that allows the passage of swallowed materials into the esophagus during swallowing. Delayed UES opening or reduced duration of opening can lead to the accumulation of pharyngeal residue, which can increase risk of aspiration. UES opening is observed through the inspection of radiographic exams, known as videofluoroscopy swallow studies (VFSSs), which expose patients to ionizing radiation and depend on subjective clinician interpretations. High resolution cervical auscultation (HRCA) is a non-invasive sensor-based technology that has been recently investigated to depict swallowing physiology. HRCA has been proposed for detecting UES opening duration through a deep learning framework. However, the proposed framework was only validated over swallows from patients. For such an algorithm to be robust, it has to be proven equally reliable for the detection of UES opening duration in swallows from both patients and healthy subjects. In this study, we intend to investigate the robustness of the HRCA-based framework to detect the UES opening in signals collected from a diverse population. The framework showed comparable performance regarding the UES opening detection with an average area under the ROC curve of 95%. The results indicate that the HRCA-based UES opening detection can provide superior performance on swallows from diverse populations which demonstrates the clinical potential of HRCA as a non-invasive swallowing assessment tool.

Characterizing Swallows From People With Neurodegenerative Diseases Using High-Resolution Cervical Auscultation Signals and Temporal and Spatial Swallow Kinematic Measurements.

Abstract: 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 < .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.

Mobility of Older Adults: Gait Quality Measures Are Associated With Life-Space Assessment Scores.

Abstract: 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 <.05 and marginally related, ρ p = -.12 (regularity V), ρ p = .11 (smoothness AP), and ρ p = -.11 (step-time variability), all p <.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 < .004. CONCLUSIONS Gait quality measures taken in the clinic are associated with real-life mobility in the community.

Establishing Reference Values for Temporal Kinematic Swallow Events Across the Lifespan in Healthy Community Dwelling Adults Using High-Resolution Cervical Auscultation

Abstract: 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 < 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.

Anterior–posterior distension of maximal upper esophageal sphincter opening is correlated with high-resolution cervical auscultation signal features

Abstract: 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.

Characterizing Effortful Swallows from Healthy Community Dwelling Adults Across the Lifespan Using High-Resolution Cervical Auscultation Signals and MBSImP Scores: A Preliminary Study

Abstract: 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 < 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.

Single neuron-based neural networks are as efficient as dense deep neural networks in binary and multi-class recognition problems

Abstract: Recent advances in neuroscience have revealed many principles about neural processing. In particular, many biological systems were found to reconfigure/recruit single neurons to generate multiple kinds of decisions. Such findings have the potential to advance our understanding of the design and optimization process of artificial neural networks. Previous work demonstrated that dense neural networks are needed to shape complex decision surfaces required for AI-level recognition tasks. We investigate the ability to model high dimensional recognition problems using single or several neurons networks that are relatively easier to train. By employing three datasets, we test the use of a population of single neuron networks in performing multi-class recognition tasks. Surprisingly, we find that sparse networks can be as efficient as dense networks in both binary and multi-class tasks. Moreover, single neuron networks demonstrate superior performance in binary classification scheme and competing results when combined for multi-class recognition.

System and method of decoding a barcode using machine learning

Abstract: A method of decoding a barcode includes capturing an image of the barcode, dividing the image of the barcode into a plurality of rows and columns, each column corresponding to one encoded character of the barcode, using a number of trained image classifiers on each row to determine a predicted character for each column of the row, for each column, determining an output character for the column based on each of the predicted characters associated with the column, and for each column, outputting the output character. Also, a system for decoding a barcode that implements the method.