Review of Deep Learning-Based Atrial Fibrillation Detection Studies

Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intel...

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Autores principales:	Fatma Murat, Ferhat Sadak, Ozal Yildirim, Muhammed Talo, Ender Murat, Murat Karabatak, Yakup Demir, Ru-San Tan, U. Rajendra Acharya
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	atrial fibrillation ECG deep learning deep neural networks arrhythmia detection Medicine R
Acceso en línea:	https://doaj.org/article/ebd143e1e70f4fdcb38ec302c184a87b
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id	oai:doaj.org-article:ebd143e1e70f4fdcb38ec302c184a87b
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spelling	oai:doaj.org-article:ebd143e1e70f4fdcb38ec302c184a87b2021-11-11T16:27:08ZReview of Deep Learning-Based Atrial Fibrillation Detection Studies10.3390/ijerph1821113021660-46011661-7827https://doaj.org/article/ebd143e1e70f4fdcb38ec302c184a87b2021-10-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11302https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.Fatma MuratFerhat SadakOzal YildirimMuhammed TaloEnder MuratMurat KarabatakYakup DemirRu-San TanU. Rajendra AcharyaMDPI AGarticleatrial fibrillationECGdeep learningdeep neural networksarrhythmia detectionMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11302, p 11302 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	atrial fibrillation ECG deep learning deep neural networks arrhythmia detection Medicine R
spellingShingle	atrial fibrillation ECG deep learning deep neural networks arrhythmia detection Medicine R Fatma Murat Ferhat Sadak Ozal Yildirim Muhammed Talo Ender Murat Murat Karabatak Yakup Demir Ru-San Tan U. Rajendra Acharya Review of Deep Learning-Based Atrial Fibrillation Detection Studies
description	Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.
format	article
author	Fatma Murat Ferhat Sadak Ozal Yildirim Muhammed Talo Ender Murat Murat Karabatak Yakup Demir Ru-San Tan U. Rajendra Acharya
author_facet	Fatma Murat Ferhat Sadak Ozal Yildirim Muhammed Talo Ender Murat Murat Karabatak Yakup Demir Ru-San Tan U. Rajendra Acharya
author_sort	Fatma Murat
title	Review of Deep Learning-Based Atrial Fibrillation Detection Studies
title_short	Review of Deep Learning-Based Atrial Fibrillation Detection Studies
title_full	Review of Deep Learning-Based Atrial Fibrillation Detection Studies
title_fullStr	Review of Deep Learning-Based Atrial Fibrillation Detection Studies
title_full_unstemmed	Review of Deep Learning-Based Atrial Fibrillation Detection Studies
title_sort	review of deep learning-based atrial fibrillation detection studies
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ebd143e1e70f4fdcb38ec302c184a87b
work_keys_str_mv	AT fatmamurat reviewofdeeplearningbasedatrialfibrillationdetectionstudies AT ferhatsadak reviewofdeeplearningbasedatrialfibrillationdetectionstudies AT ozalyildirim reviewofdeeplearningbasedatrialfibrillationdetectionstudies AT muhammedtalo reviewofdeeplearningbasedatrialfibrillationdetectionstudies AT endermurat reviewofdeeplearningbasedatrialfibrillationdetectionstudies AT muratkarabatak reviewofdeeplearningbasedatrialfibrillationdetectionstudies AT yakupdemir reviewofdeeplearningbasedatrialfibrillationdetectionstudies AT rusantan reviewofdeeplearningbasedatrialfibrillationdetectionstudies AT urajendraacharya reviewofdeeplearningbasedatrialfibrillationdetectionstudies
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Review of Deep Learning-Based Atrial Fibrillation Detection Studies

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