End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study. Issue 2 (16th April 2021)

• Record Type: Journal Article
• Authors: Coppock, Harry; Gaskell, Alex; Tzirakis, Panagiotis; Baird, Alice; Jones, Lyn; Schuller, Björn
• Is Part Of: BMJ innovations. Volume 7:Issue 2(2021)
• Page Start: 356
• Page End: 362
• Publication Date: 2021-04-16
• Subjects: COVID-19 -- virus diseases -- diagnosis; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjinnov-2021-000668
• Languages: English
• ISSNs: 2055-8074

Abstract

Abstract : 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.