End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

Author:

Coppock HarryORCID,Gaskell Alex,Tzirakis Panagiotis,Baird Alice,Jones Lyn,Schuller Björn

Abstract

BackgroundSince 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.MethodsThis 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.ResultsOur 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.ConclusionThis 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.

Funder

EPSRC Center for Doctoral Training in High Performance Embedded and Distributed Systems

DFG under AUDI0NOMOUS (Agent-based Unsupervised Deep Interactive 0-shot-learning Networks Optimising Machines’ Ontological Understanding of Sound, Reinhart Koselleck-Project

Imperial College London Teaching Scholarship

UK Research and Innovation Centre for Doctoral Training in Safe and Trusted Artificial Intelligence

Publisher

BMJ

Subject

General Medicine

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