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QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds

Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo...

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Autores principales: Rahman, Tawsifur, Ibtehaz, Nabil, Khandakar, Amith, Hossain, Md Sakib Abrar, Mekki, Yosra Magdi Salih, Ezeddin, Maymouna, Bhuiyan, Enamul Haque, Ayari, Mohamed Arselene, Tahir, Anas, Qiblawey, Yazan, Mahmud, Sakib, Zughaier, Susu M., Abbas, Tariq, Al-Maadeed, Somaya, Chowdhury, Muhammad E. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028864/
https://www.ncbi.nlm.nih.gov/pubmed/35453968
http://dx.doi.org/10.3390/diagnostics12040920
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author Rahman, Tawsifur
Ibtehaz, Nabil
Khandakar, Amith
Hossain, Md Sakib Abrar
Mekki, Yosra Magdi Salih
Ezeddin, Maymouna
Bhuiyan, Enamul Haque
Ayari, Mohamed Arselene
Tahir, Anas
Qiblawey, Yazan
Mahmud, Sakib
Zughaier, Susu M.
Abbas, Tariq
Al-Maadeed, Somaya
Chowdhury, Muhammad E. H.
author_facet Rahman, Tawsifur
Ibtehaz, Nabil
Khandakar, Amith
Hossain, Md Sakib Abrar
Mekki, Yosra Magdi Salih
Ezeddin, Maymouna
Bhuiyan, Enamul Haque
Ayari, Mohamed Arselene
Tahir, Anas
Qiblawey, Yazan
Mahmud, Sakib
Zughaier, Susu M.
Abbas, Tariq
Al-Maadeed, Somaya
Chowdhury, Muhammad E. H.
author_sort Rahman, Tawsifur
collection PubMed
description Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim—This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method—A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user’s home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results—The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion—The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease.
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spelling pubmed-90288642022-04-23 QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds Rahman, Tawsifur Ibtehaz, Nabil Khandakar, Amith Hossain, Md Sakib Abrar Mekki, Yosra Magdi Salih Ezeddin, Maymouna Bhuiyan, Enamul Haque Ayari, Mohamed Arselene Tahir, Anas Qiblawey, Yazan Mahmud, Sakib Zughaier, Susu M. Abbas, Tariq Al-Maadeed, Somaya Chowdhury, Muhammad E. H. Diagnostics (Basel) Article Problem—Since the outbreak of the COVID-19 pandemic, mass testing has become essential to reduce the spread of the virus. Several recent studies suggest that a significant number of COVID-19 patients display no physical symptoms whatsoever. Therefore, it is unlikely that these patients will undergo COVID-19 testing, which increases their chances of unintentionally spreading the virus. Currently, the primary diagnostic tool to detect COVID-19 is a reverse-transcription polymerase chain reaction (RT-PCR) test from the respiratory specimens of the suspected patient, which is invasive and a resource-dependent technique. It is evident from recent researches that asymptomatic COVID-19 patients cough and breathe in a different way than healthy people. Aim—This paper aims to use a novel machine learning approach to detect COVID-19 (symptomatic and asymptomatic) patients from the convenience of their homes so that they do not overburden the healthcare system and also do not spread the virus unknowingly by continuously monitoring themselves. Method—A Cambridge University research group shared such a dataset of cough and breath sound samples from 582 healthy and 141 COVID-19 patients. Among the COVID-19 patients, 87 were asymptomatic while 54 were symptomatic (had a dry or wet cough). In addition to the available dataset, the proposed work deployed a real-time deep learning-based backend server with a web application to crowdsource cough and breath datasets and also screen for COVID-19 infection from the comfort of the user’s home. The collected dataset includes data from 245 healthy individuals and 78 asymptomatic and 18 symptomatic COVID-19 patients. Users can simply use the application from any web browser without installation and enter their symptoms, record audio clips of their cough and breath sounds, and upload the data anonymously. Two different pipelines for screening were developed based on the symptoms reported by the users: asymptomatic and symptomatic. An innovative and novel stacking CNN model was developed using three base learners from of eight state-of-the-art deep learning CNN algorithms. The stacking CNN model is based on a logistic regression classifier meta-learner that uses the spectrograms generated from the breath and cough sounds of symptomatic and asymptomatic patients as input using the combined (Cambridge and collected) dataset. Results—The stacking model outperformed the other eight CNN networks with the best classification performance for binary classification using cough sound spectrogram images. The accuracy, sensitivity, and specificity for symptomatic and asymptomatic patients were 96.5%, 96.42%, and 95.47% and 98.85%, 97.01%, and 99.6%, respectively. For breath sound spectrogram images, the metrics for binary classification of symptomatic and asymptomatic patients were 91.03%, 88.9%, and 91.5% and 80.01%, 72.04%, and 82.67%, respectively. Conclusion—The web-application QUCoughScope records coughing and breathing sounds, converts them to a spectrogram, and applies the best-performing machine learning model to classify the COVID-19 patients and healthy subjects. The result is then reported back to the test user in the application interface. Therefore, this novel system can be used by patients in their premises as a pre-screening method to aid COVID-19 diagnosis by prioritizing the patients for RT-PCR testing and thereby reducing the risk of spreading of the disease. MDPI 2022-04-07 /pmc/articles/PMC9028864/ /pubmed/35453968 http://dx.doi.org/10.3390/diagnostics12040920 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rahman, Tawsifur
Ibtehaz, Nabil
Khandakar, Amith
Hossain, Md Sakib Abrar
Mekki, Yosra Magdi Salih
Ezeddin, Maymouna
Bhuiyan, Enamul Haque
Ayari, Mohamed Arselene
Tahir, Anas
Qiblawey, Yazan
Mahmud, Sakib
Zughaier, Susu M.
Abbas, Tariq
Al-Maadeed, Somaya
Chowdhury, Muhammad E. H.
QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds
title QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds
title_full QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds
title_fullStr QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds
title_full_unstemmed QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds
title_short QUCoughScope: An Intelligent Application to Detect COVID-19 Patients Using Cough and Breath Sounds
title_sort qucoughscope: an intelligent application to detect covid-19 patients using cough and breath sounds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9028864/
https://www.ncbi.nlm.nih.gov/pubmed/35453968
http://dx.doi.org/10.3390/diagnostics12040920
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