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
Reference33 articles.
1. Overview: systemic inflammatory response derived from lung injury caused by SARS-CoV-2 infection explains severe outcomes in COVID-19;Polidoro;Front Immunol,2020
2. A framework for biomarkers of COVID-19 based on coordination of Speech-Production subsystems;Quatieri;IEEE Open Journal of Engineering in Medicine and Biology,2020
3. Orlandic L , Teijeiro Tomás , Atienza D . The COUGHVID crowdsourcing dataset: a corpus for the study of large-scale cough analysis algorithms. arXiv 2020:11.
4. Sharma N , Krishnan P , Kumar R . Coswara – a database of breathing, cough, and voice sounds for COVID-19 diagnosis. In Proc. Interspeech, pages 4811–4815, Shanghai, China, 2020:4811–5.
5. Bagad P , Dalmia A , Doshi J . Cough against COVID: evidence of COVID-19 signature in cough sounds. arXiv 2020:12.
Cited by
81 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献