Identifying individuals with recent COVID-19 through voice classification using deep learning

Abstract Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent res...

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Autores principales:	Pichatorn Suppakitjanusant, Somnuek Sungkanuparph, Thananya Wongsinin, Sirapong Virapongsiri, Nittaya Kasemkosin, Laor Chailurkit, Boonsong Ongphiphadhanakul
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b251be9ae3604d9d95be9d83337acd9a
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spelling	oai:doaj.org-article:b251be9ae3604d9d95be9d83337acd9a2021-12-02T19:17:00ZIdentifying individuals with recent COVID-19 through voice classification using deep learning10.1038/s41598-021-98742-x2045-2322https://doaj.org/article/b251be9ae3604d9d95be9d83337acd9a2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98742-xhttps://doaj.org/toc/2045-2322Abstract Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an ‘ah’ sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic ‘ah’ sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.Pichatorn SuppakitjanusantSomnuek SungkanuparphThananya WongsininSirapong VirapongsiriNittaya KasemkosinLaor ChailurkitBoonsong OngphiphadhanakulNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pichatorn Suppakitjanusant Somnuek Sungkanuparph Thananya Wongsinin Sirapong Virapongsiri Nittaya Kasemkosin Laor Chailurkit Boonsong Ongphiphadhanakul Identifying individuals with recent COVID-19 through voice classification using deep learning
description	Abstract Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration in COVID-19 patients after the recent resolution of disease using deep learning. The study was a prospective study of 76 post COVID-19 patients and 40 healthy individuals. The diagnoses of post COVID-19 patients were based on more than the eighth week after onset of symptoms. Voice samples of an ‘ah’ sound, coughing sound and a polysyllabic sentence were collected and preprocessed to log-mel spectrogram. Transfer learning using the VGG19 pre-trained convolutional neural network was performed with all voice samples. The performance of the model using the polysyllabic sentence yielded the highest classification performance of all models. The coughing sound produced the lowest classification performance while the ability of the monosyllabic ‘ah’ sound to predict the recent COVID-19 fell between the other two vocalizations. The model using the polysyllabic sentence achieved 85% accuracy, 89% sensitivity, and 77% specificity. In conclusion, deep learning is able to detect the subtle change in voice features of COVID-19 patients after recent resolution of the disease.
format	article
author	Pichatorn Suppakitjanusant Somnuek Sungkanuparph Thananya Wongsinin Sirapong Virapongsiri Nittaya Kasemkosin Laor Chailurkit Boonsong Ongphiphadhanakul
author_facet	Pichatorn Suppakitjanusant Somnuek Sungkanuparph Thananya Wongsinin Sirapong Virapongsiri Nittaya Kasemkosin Laor Chailurkit Boonsong Ongphiphadhanakul
author_sort	Pichatorn Suppakitjanusant
title	Identifying individuals with recent COVID-19 through voice classification using deep learning
title_short	Identifying individuals with recent COVID-19 through voice classification using deep learning
title_full	Identifying individuals with recent COVID-19 through voice classification using deep learning
title_fullStr	Identifying individuals with recent COVID-19 through voice classification using deep learning
title_full_unstemmed	Identifying individuals with recent COVID-19 through voice classification using deep learning
title_sort	identifying individuals with recent covid-19 through voice classification using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b251be9ae3604d9d95be9d83337acd9a
work_keys_str_mv	AT pichatornsuppakitjanusant identifyingindividualswithrecentcovid19throughvoiceclassificationusingdeeplearning AT somnueksungkanuparph identifyingindividualswithrecentcovid19throughvoiceclassificationusingdeeplearning AT thananyawongsinin identifyingindividualswithrecentcovid19throughvoiceclassificationusingdeeplearning AT sirapongvirapongsiri identifyingindividualswithrecentcovid19throughvoiceclassificationusingdeeplearning AT nittayakasemkosin identifyingindividualswithrecentcovid19throughvoiceclassificationusingdeeplearning AT laorchailurkit identifyingindividualswithrecentcovid19throughvoiceclassificationusingdeeplearning AT boonsongongphiphadhanakul identifyingindividualswithrecentcovid19throughvoiceclassificationusingdeeplearning
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Identifying individuals with recent COVID-19 through voice classification using deep learning

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