Detecting COVID-19 from breathing and coughing sounds using deep neural networks

Mina A. Nessiem, Mostafa M. Mohamed, Harry Coppock, Alexander Gaskell, Bjorn W. Schuller

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

32 Zitate (Scopus)

Abstract

The COVID-19 pandemic has affected the world unevenly; while industrial economies have been able to produce the tests necessary to track the spread of the virus and mostly avoided complete lockdowns, developing countries have faced issues with testing capacity. In this paper, we explore the usage of deep learning models as a ubiquitous, low-cost, pre-testing method for detecting COVID-19 from audio recordings of breathing or coughing taken with mobile devices or via the web. We adapt an ensemble of Convolutional Neural Networks that utilise raw breathing and coughing audio and spectrograms to classify if a speaker is infected with COVID-19 or not. The different models are obtained via automatic hyperparameter tuning using Bayesian Optimisation combined with HyperBand. The proposed method outperforms a traditional baseline approach by a large margin. Ultimately, it achieves an Unweighted Average Recall (UAR) of 74.9%, or an Area Under ROC Curve (AUC) of 80.7% by ensembling neural networks, considering the best test set result across breathing and coughing in a strictly subject independent manner. In isolation, breathing sounds thereby appear slightly better suited than coughing ones (76.1% vs 73.7% UAR).

OriginalspracheEnglisch
TitelProceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021
Redakteure/-innenJoao Rafael Almeida, Alejandro Rodriguez Gonzalez, Linlin Shen, Bridget Kane, Agma Traina, Paolo Soda, Jose Luis Oliveira
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten183-188
Seitenumfang6
ISBN (elektronisch)9781665441216
DOIs
PublikationsstatusVeröffentlicht - Juni 2021
Extern publiziertJa
Veranstaltung34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021 - Virtual, Online
Dauer: 7 Juni 20219 Juni 2021

Publikationsreihe

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Band2021-June
ISSN (Print)1063-7125

Konferenz

Konferenz34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021
OrtVirtual, Online
Zeitraum7/06/219/06/21

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