Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

Abstract Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) reco...

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Autores principales:	Pietro Melzi, Ruben Tolosana, Alberto Cecconi, Ancor Sanz-Garcia, Guillermo J. Ortega, Luis Jesus Jimenez-Borreguero, Ruben Vera-Rodriguez
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d3fe7ade268d446db2798bffbd1c1372
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spelling	oai:doaj.org-article:d3fe7ade268d446db2798bffbd1c13722021-11-28T12:16:14ZAnalyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization10.1038/s41598-021-02179-12045-2322https://doaj.org/article/d3fe7ade268d446db2798bffbd1c13722021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02179-1https://doaj.org/toc/2045-2322Abstract Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.Pietro MelziRuben TolosanaAlberto CecconiAncor Sanz-GarciaGuillermo J. OrtegaLuis Jesus Jimenez-BorregueroRuben Vera-RodriguezNature 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 Pietro Melzi Ruben Tolosana Alberto Cecconi Ancor Sanz-Garcia Guillermo J. Ortega Luis Jesus Jimenez-Borreguero Ruben Vera-Rodriguez Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
description	Abstract Atrial fibrillation (AF) is an abnormal heart rhythm, asymptomatic in many cases, that causes several health problems and mortality in population. This retrospective study evaluates the ability of different AI-based models to predict future episodes of AF from electrocardiograms (ECGs) recorded during normal sinus rhythm. Patients are divided into two classes according to AF occurrence or sinus rhythm permanence along their several ECGs registry. In the constrained scenario of balancing the age distributions between classes, our best AI model predicts future episodes of AF with area under the curve (AUC) 0.79 (0.72–0.86). Multiple scenarios and age-sex-specific groups of patients are considered, achieving best performance of prediction for males older than 70 years. These results point out the importance of considering different demographic groups in the analysis of AF prediction, showing considerable performance gaps among them. In addition to the demographic analysis, we apply feature visualization techniques to identify the most important portions of the ECG signals in the task of AF prediction, improving this way the interpretability and understanding of the AI models. These results and the simplicity of recording ECGs during check-ups add feasibility to clinical applications of AI-based models.
format	article
author	Pietro Melzi Ruben Tolosana Alberto Cecconi Ancor Sanz-Garcia Guillermo J. Ortega Luis Jesus Jimenez-Borreguero Ruben Vera-Rodriguez
author_facet	Pietro Melzi Ruben Tolosana Alberto Cecconi Ancor Sanz-Garcia Guillermo J. Ortega Luis Jesus Jimenez-Borreguero Ruben Vera-Rodriguez
author_sort	Pietro Melzi
title	Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
title_short	Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
title_full	Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
title_fullStr	Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
title_full_unstemmed	Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
title_sort	analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ecgs including demographics and feature visualization
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d3fe7ade268d446db2798bffbd1c1372
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Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization

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