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An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance di...

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Autor principal: Attallah, Omneya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138764/
https://www.ncbi.nlm.nih.gov/pubmed/35624600
http://dx.doi.org/10.3390/bios12050299
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author Attallah, Omneya
author_facet Attallah, Omneya
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description Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19.
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spelling pubmed-91387642022-05-28 An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques Attallah, Omneya Biosensors (Basel) Article Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19. MDPI 2022-05-05 /pmc/articles/PMC9138764/ /pubmed/35624600 http://dx.doi.org/10.3390/bios12050299 Text en © 2022 by the author. 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
Attallah, Omneya
An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques
title An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques
title_full An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques
title_fullStr An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques
title_full_unstemmed An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques
title_short An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques
title_sort intelligent ecg-based tool for diagnosing covid-19 via ensemble deep learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9138764/
https://www.ncbi.nlm.nih.gov/pubmed/35624600
http://dx.doi.org/10.3390/bios12050299
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