End-2-End convolutional neural network enables COVID-19 Detection from Breath & Cough Audio: a pilot study

Background: Since the emergence of COVID-19 in December 2019, multi-disciplinary research teams have wrestled with how best to control the pandemic in light of it’s 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 dataset consisting of 355 crowdsourced participants, achieving a ROC-AUC of 0.846 on the task of COVID 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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@article{coppock2021end2end,
      title={End-2-End COVID-19 Detection from Breath & Cough Audio}, 
      author={Harry Coppock and Alexander Gaskell and Panagiotis Tzirakis and Alice Baird and Lyn Jones and Björn W. Schuller},
      year={2021},
      eprint={2102.08359},
      archivePrefix={BMJ innovations},
      primaryClass={cs.SD}
}