2021
DOI: 10.1136/bmjinnov-2021-000668
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End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

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 … Show more

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Cited by 90 publications
(65 citation statements)
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References 25 publications
(25 reference statements)
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“…In Hershey et al's ( 48 ) study,ResNet has outperformed other CNNs for audio classification on the Audio Set ( 49 ). A ResNet based model is constructed for COVID-19 detection from breath and cough audio signals ( 50 ).…”
Section: Automatic Covid-19 Detectionmentioning
confidence: 99%
“…In Hershey et al's ( 48 ) study,ResNet has outperformed other CNNs for audio classification on the Audio Set ( 49 ). A ResNet based model is constructed for COVID-19 detection from breath and cough audio signals ( 50 ).…”
Section: Automatic Covid-19 Detectionmentioning
confidence: 99%
“…High cost and availability of CT-Scanners is also an issue. Research has also taken place in exploring the use of cough sounds for COVID-19 diagnosis using NLP (Natural Language Processing) [26][27][28].…”
Section: Introductionmentioning
confidence: 99%
“…Taking into consideration the high percentage of asymptomatic yet contagious COVID-19 patients, researchers have argued that forced-cough (i.e. voluntary cough [11] ) keeps the same biomarker potential of the spontaneous one, as data have wildly proved [ [10] , [11] , [12] , [14] , [15] , [16] , [17] , [18] , [19] ] . In fact, half of the asymptomatic cases present CT abnormalities [3] .…”
Section: Introductionmentioning
confidence: 99%
“…Thus a single neural network framework is capable of performing feature extraction, feature selection, dimensionality reduction and prediction by optimizing each single module. Other works such as [ 9 , 16 , 19 ] used end-to-end training and deep neural networks, however these works lack the optimization of the spectrogram's filter generation as an internal process. Moreover, no attention mechanisms are used to extract relevant patterns.…”
Section: Introductionmentioning
confidence: 99%