EFFECT OF SEGMENTAL VIBRATION ON HAND AND PINCH GRIP STRENGTHS IN HEALTHY SUBJECTS

Handgrip and pinch strengths are important markers in many sports as well as in determining health status. Many interventions have been proposed for increasing hand muscle strength. The aim of this study was to investigate the effect of segmental hand vibration on hand and pinch grip strengths. Ninety-two healthy university students were randomly assigned into two equal groups. By the end of the study, Group (A) and (B) consisted of 40 and 37 participants, respectively. The measurements consisted of the hand grip and pinch grip strengths using electronic dynamometer, measured in Kg, before, after three weeks, and after six weeks of training. Group (A) underwent isometric exercise training using hand gripper as follows: 4 seconds maximum grip, release for 2 seconds, repeated for 1 minute for three sets and with 3 minutes rest in between. Group (B) had the same exercise implemented in group (A) with the addition of five minutes of segmental vibration on both upper limb with 30Hz and amplitude of 2mm. The training was done two times per week for six weeks. Results revealed that both groups did demonstrate significant increase in hand and grip strengths after six weeks (p.05). It can be concluded that, segmental upper limb vibration does not have additional effect over isometric muscle training alone on hand grip and pinch grip strengths

Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia

IntroductionPost-stroke dysphagia is common and associated with significant morbidity and mortality, rendering bedside screening of significant clinical importance. Using voice as a biomarker coupled with deep learning has the potential to improve patient access to screening and mitigate the subjectivity associated with detecting voice change, a component of several validated screening protocols.MethodsIn this single-center study, we developed a proof-of-concept model for automated dysphagia screening and evaluated the performance of this model on training and testing cohorts. Patients were admitted to a comprehensive stroke center, where primary English speakers could follow commands without significant aphasia and participated on a rolling basis. The primary outcome was classification either as a pass or fail equivalent using a dysphagia screening test as a label. Voice data was recorded from patients who spoke a standardized set of vowels, words, and sentences from the National Institute of Health Stroke Scale. Seventy patients were recruited and 68 were included in the analysis, with 40 in training and 28 in testing cohorts, respectively. Speech from patients was segmented into 1,579 audio clips, from which 6,655 Mel-spectrogram images were computed and used as inputs for deep-learning models (DenseNet and ConvNext, separately and together). Clip-level and participant-level swallowing status predictions were obtained through a voting method.ResultsThe models demonstrated clip-level dysphagia screening sensitivity of 71% and specificity of 77% (F1 = 0.73, AUC = 0.80 [95% CI: 0.78–0.82]). At the participant level, the sensitivity and specificity were 89 and 79%, respectively (F1 = 0.81, AUC = 0.91 [95% CI: 0.77–1.05]).DiscussionThis study is the first to demonstrate the feasibility of applying deep learning to classify vocalizations to detect post-stroke dysphagia. Our findings suggest potential for enhancing dysphagia screening in clinical settings. https://github.com/UofTNeurology/masa-open-source

The ear: Gateway to the brain. Exploring the Feasibility of a novel in-ear electroencephalography device in an auditory brain-computer interface

A brain-computer interface (BCI) enables communication using signals directly from the brain. Electroencephalography (EEG) is often used to collect these signals and commonly involves scalp-based electrodes which are conspicuous, and uncomfortable over long periods. In-ear EEG devices address many of these shortcomings and can discreetly measure brain activity from within the ear canal. We designed an in-ear EEG device usable with a commercial EEG amplifier and tested this device in an auditory BCI involving auditory evoked potentials (AEPs) and the auditory steady state response (ASSR). Using scalp electrodes, AEP accuracy was ~ 79 %, and focus-modulated ASSR accuracies were above chance in 3 of 7 participants. Equivalent neural responses were evidenced in the in-ear recordings. However, classification outcomes were poor, reflecting challenges with in-ear measurement and the reduced number of electrodes. This study highlights the need for further research into device morphologies and paradigms for in-ear EEG.M.A.S

IBBME Discovery: Biomedical Engineering-based Iterative Learning in a High School STEM Curriculum (Evaluation)

© 2018 American Society for Engineering Education.Senior high school students often struggle with recognizing the link between human health care and engineering, resulting in limited recruitment for post-secondary biomedical engineering (BME) study. To enhance student comprehension and recruitment in the field, BME graduate student instructors have developed and launched Discovery, a collaborative high school outreach program that promotes and engages students in the application of science, technology, engineering, and math (STEM) concepts. The program offers a unique, immersive semester-long practicum that complements classroom curriculum but is delivered within university facilities. Further to this, BME graduate students have the opportunity to develop and deliver STEM curriculum directly aligned with their thesis research. The overall goal of the program is to immerse high school science students in inquiry-based experiential learning in a post-secondary BME environment, enhancing BME literacy and stimulating pursuit of post-secondary STEM study. This program is a collaboration between graduate student instructors and science educators from one local public high school. Each semester, approximately 65 secondary STEM students, 4 educators, 15 graduate student instructors, and 2 faculty members are involved in Discovery. Small student groups work in a capstone format, incorporating iterative design principles and the scientific method to address thematically-related but subject-specific research projects that satisfy curriculum requirements. Educators assign 10-15% of semester course grades to deliverables and quantitatively assess student comprehension. The semester culminates in a final symposium where students present their findings in scientific poster format. Discovery is unique in its delivery of iterative design to a class cohort accompanied by their educator and carries the benefit of removing socio-economic barriers to student learning and success. High school educators further benefit through co-instruction with graduate instructors within university facilities, increasing student comfort within laboratory environments. High-school educators have identified difficulties with student involvement in the regular classroom. Comparatively, to date, all students have successfully engaged in the various Discovery activities. During the pilot year, > 85% of participants exhibited perfect Discovery attendance; these students demonstrated absence for ~ 10% of classes in their school environment. Students view this experience as an integral part of their classroom curriculum and are both excited and engaged in their scientific outcomes. In post-hoc surveys, over 75% of student participants stated that this program impacted their pursuit of future studies in STEM, indicating a greater understanding of BME theory and practice, while anecdotally graduate instructors indicated that their pedagogical training greatly improved.This program was financially supported by the IBBME and the NSERC PromoScience program

Data_Sheet_1_Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia.docx

IntroductionPost-stroke dysphagia is common and associated with significant morbidity and mortality, rendering bedside screening of significant clinical importance. Using voice as a biomarker coupled with deep learning has the potential to improve patient access to screening and mitigate the subjectivity associated with detecting voice change, a component of several validated screening protocols.MethodsIn this single-center study, we developed a proof-of-concept model for automated dysphagia screening and evaluated the performance of this model on training and testing cohorts. Patients were admitted to a comprehensive stroke center, where primary English speakers could follow commands without significant aphasia and participated on a rolling basis. The primary outcome was classification either as a pass or fail equivalent using a dysphagia screening test as a label. Voice data was recorded from patients who spoke a standardized set of vowels, words, and sentences from the National Institute of Health Stroke Scale. Seventy patients were recruited and 68 were included in the analysis, with 40 in training and 28 in testing cohorts, respectively. Speech from patients was segmented into 1,579 audio clips, from which 6,655 Mel-spectrogram images were computed and used as inputs for deep-learning models (DenseNet and ConvNext, separately and together). Clip-level and participant-level swallowing status predictions were obtained through a voting method.ResultsThe models demonstrated clip-level dysphagia screening sensitivity of 71% and specificity of 77% (F1 = 0.73, AUC = 0.80 [95% CI: 0.78–0.82]). At the participant level, the sensitivity and specificity were 89 and 79%, respectively (F1 = 0.81, AUC = 0.91 [95% CI: 0.77–1.05]).DiscussionThis study is the first to demonstrate the feasibility of applying deep learning to classify vocalizations to detect post-stroke dysphagia. Our findings suggest potential for enhancing dysphagia screening in clinical settings. https://github.com/UofTNeurology/masa-opensource</p