RT Journal Article
SR Electronic
T1 End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study
JF BMJ Innovations
JO BMJ Innov
FD All India Institute of Medical Sciences
SP 356
OP 362
DO 10.1136/bmjinnov-2021-000668
VO 7
IS 2
A1 Coppock, Harry
A1 Gaskell, Alex
A1 Tzirakis, Panagiotis
A1 Baird, Alice
A1 Jones, Lyn
A1 Schuller, Björn
YR 2021
UL http://innovations.bmj.com/content/7/2/356.abstract
AB Background Since the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.Methods This study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.Results Our model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.Conclusion This study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.Data are available upon reasonable request.