A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

Abstract Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal...

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Autores principales:	Yong-Soo Baek, Sang-Chul Lee, Wonik Choi, Dae-Hyeok Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/48682c14524e4e05a6ad4f592d0e3021
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spelling	oai:doaj.org-article:48682c14524e4e05a6ad4f592d0e30212021-12-02T17:41:10ZA new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm10.1038/s41598-021-92172-52045-2322https://doaj.org/article/48682c14524e4e05a6ad4f592d0e30212021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92172-5https://doaj.org/toc/2045-2322Abstract Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.Yong-Soo BaekSang-Chul LeeWonik ChoiDae-Hyeok KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yong-Soo Baek Sang-Chul Lee Wonik Choi Dae-Hyeok Kim A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
description	Abstract Atrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.
format	article
author	Yong-Soo Baek Sang-Chul Lee Wonik Choi Dae-Hyeok Kim
author_facet	Yong-Soo Baek Sang-Chul Lee Wonik Choi Dae-Hyeok Kim
author_sort	Yong-Soo Baek
title	A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
title_short	A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
title_full	A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
title_fullStr	A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
title_full_unstemmed	A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
title_sort	new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/48682c14524e4e05a6ad4f592d0e3021
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A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

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