Acoustics team wins international machine learning challenge
Our entry into the 2022 George B. Moody PhysioNet challenge on automated heart sound detection won first prize. The George B. Moody PhysioNet Challenge (formerly the...
Andrew presents our paper on detecting equine heart murmurs at the BEVA Congress in Liverpool
Up to one-third of adult horses have valvular heart disease, which produces characteristic murmur sounds that can be...
Intelligent stethoscope study featured on BBC News
Our research on machine learning techniques for automating the detection of valvular heart disease (VHD) was featured...
Intelligent stethoscope demonstrated at Heart Valve Voice Westminster event
We attended the Heart Valve Voice screening event in Westminster to talk to MPs and stakeholders about our research on...
Research
Publications
Narrative review of the role of artificial intelligence to improve aortic valve disease management. In: Journal of Thoracic Disease, 13 (1), pp. 396-404, 2021, ISSN: 2077-6624.
A theory for stethoscope acoustics. In: Proceedings of the 23rd international congress on acoustics, pp. 4991–4998, 2019, (Best Student Paper Award).
The sound produced by a dripping tap is driven by resonant oscillations of an entrapped air bubble. In: Scientific Reports, 8 (1), pp. 1-12, 2018.
DropConnected neural networks trained on time-frequency and inter-beat features for classifying heart sounds. In: Physiological Measurement, 38 (8), pp. 1645-1657, 2017.
The elastic theory of shells using geometric algebra. In: Royal Society Open Science, 4 (170065), pp. 1-15, 2017.
Mice produce ultrasonic vocalizations by intra-laryngeal planar
impinging jets. In: Current Biology, 26 (R1--R3), 2016.
An intelligent stethoscope to detect valvular heart disease
ITV News East Anglia report on our research study
