End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study
Open accessAbstract
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.
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COVID-19 has been studied using voice, breathing, and cough sounds.
“COVID-19 has been studied using different audio sounds, including voice, breathing and cough sounds [10,29,79,80].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [29]
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Self-citation
- Authors
- Coppock, H., Gaskell, A., Tzirakis, P., Baird, A., Jones, L., & Schuller, B.
- Affiliations
- Imperial College London (GB)
- Computing, Imperial College London, London, UK
- University of Augsburg (DE)
- North Bristol NHS Trust (GB)
- Corresponding author
- Coppock, Harry
- Venue
- BMJ Innovations
- Publisher
- BMJ
- Published
- 2021-04
- Volume
- 7
- Issue
- 2
- Pages
- 356-362
- ISSN
- 2055-642X (ISSN-L)
- 2055-8074
- OpenAlex
- W3152531055
- Cited by
- 108
- References
- 35
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 10 · 2023: 30 · 2022: 40 · 2021: 12
- FWCI
- 15.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- BMJ Innovations (publishedVersion)
- PubMed (publishedVersion)
- OPUS (Augsburg University) (submittedVersion, cc-by-nc)
- Funders
- EPSRC Center for Doctoral Training in High Performance Embedded and Distributed Systems (EP/L016796/1)
- DFG under AUDI0NOMOUS (Agent-based Unsupervised Deep Interactive 0-shot-learning Networks Optimising Machines’ Ontological Understanding of Sound, Reinhart Koselleck-Project (442218748)
- Imperial College London Teaching Scholarship (NA)
- UK Research and Innovation Centre for Doctoral Training in Safe and Trusted Artificial Intelligence (EP/S023356/1)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- COVID-19 detection, convolutional neural networks, asymptomatic COVID-19, non-invasive diagnosis
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