32 citations · 37 across the 4 of their papers we have counts for
5 papers
Heart Rate Extraction from Abdominal Audio Signals
Jake Stuchbury-Wass, Erika Bondareva, Kayla-Jade Butkow +3
Abdominal sounds (ABS) have been traditionally used for assessing gastrointestinal (GI) disorders. However, the assessment requires a trained medical professional to perform multip…
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
Ting Dang, Jing Han, Tong Xia +9
Recent work has shown the potential of using audio data (eg, cough, breathing, and voice) in the screening for COVID-19. However, these approaches only focus on one-off detection a…
Earables for Detection of Bruxism: a Feasibility Study
Erika Bondareva, Elín Rós Hauksdóttir, Cecilia Mascolo
Bruxism is a disorder characterised by teeth grinding and clenching, and many bruxism sufferers are not aware of this disorder until their dental health professional notices perman…
Segmentation-free Heart Pathology Detection Using Deep Learning
Erika Bondareva, Jing Han, William Bradlow +1
Cardiovascular (CV) diseases are the leading cause of death in the world, and auscultation is typically an essential part of a cardiovascular examination. The ability to diagnose a…
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
Jing Han, Tong Xia, Dimitris Spathis +9
Researchers have been battling with the question of how we can identify Coronavirus disease (COVID-19) cases efficiently, affordably and at scale. Recent work has shown how audio b…