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

Autores
Melzi, Pietro; Tolosana, Ruben; Cecconi, Alberto; Sanz Garcia, Ancor; Ortega, Guillermo José; Jimenez Borreguero, Luis Jesus; Vera Rodriguez, Ruben
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Melzi, Pietro. Universidad Autónoma de Madrid; España
Fil: Tolosana, Ruben. Universidad Autónoma de Madrid; España
Fil: Cecconi, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Sanz Garcia, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Ortega, Guillermo José. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Jimenez Borreguero, Luis Jesus. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Vera Rodriguez, Ruben. Universidad Autónoma de Madrid; España
Materia
deep learning
ecg
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/164586
Acceder
id CONICETDig_fb7e9189d48edc2fe3a10295232b8467
oai_identifier_str oai:ri.conicet.gov.ar:11336/164586
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualizationMelzi, PietroTolosana, RubenCecconi, AlbertoSanz Garcia, AncorOrtega, Guillermo JoséJimenez Borreguero, Luis JesusVera Rodriguez, Rubendeep learningecghttps://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3Atrial 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.Fil: Melzi, Pietro. Universidad Autónoma de Madrid; EspañaFil: Tolosana, Ruben. Universidad Autónoma de Madrid; EspañaFil: Cecconi, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Sanz Garcia, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Ortega, Guillermo José. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Jimenez Borreguero, Luis Jesus. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; EspañaFil: Vera Rodriguez, Ruben. Universidad Autónoma de Madrid; EspañaNature Publishing Group2021-11-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/164586Melzi, Pietro; Tolosana, Ruben; Cecconi, Alberto; Sanz Garcia, Ancor; Ortega, Guillermo José; et al.; Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization; Nature Publishing Group; Scientific Reports; 11; 1; 23-11-2021; 1-102045-23222045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-021-02179-1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:30:32Zoai:ri.conicet.gov.ar:11336/164586instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:30:32.39CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
title Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
spellingShingle Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization
Melzi, Pietro
deep learning
ecg
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
dc.creator.none.fl_str_mv Melzi, Pietro
Tolosana, Ruben
Cecconi, Alberto
Sanz Garcia, Ancor
Ortega, Guillermo José
Jimenez Borreguero, Luis Jesus
Vera Rodriguez, Ruben
author Melzi, Pietro
author_facet Melzi, Pietro
Tolosana, Ruben
Cecconi, Alberto
Sanz Garcia, Ancor
Ortega, Guillermo José
Jimenez Borreguero, Luis Jesus
Vera Rodriguez, Ruben
author_role author
author2 Tolosana, Ruben
Cecconi, Alberto
Sanz Garcia, Ancor
Ortega, Guillermo José
Jimenez Borreguero, Luis Jesus
Vera Rodriguez, Ruben
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv deep learning
ecg
topic deep learning
ecg
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv 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.
Fil: Melzi, Pietro. Universidad Autónoma de Madrid; España
Fil: Tolosana, Ruben. Universidad Autónoma de Madrid; España
Fil: Cecconi, Alberto. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Sanz Garcia, Ancor. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Ortega, Guillermo José. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Jimenez Borreguero, Luis Jesus. Universidad Autonoma de Madrid. Hospital Universitario de la Princesa; España
Fil: Vera Rodriguez, Ruben. Universidad Autónoma de Madrid; España
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021-11-23
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/164586
Melzi, Pietro; Tolosana, Ruben; Cecconi, Alberto; Sanz Garcia, Ancor; Ortega, Guillermo José; et al.; Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization; Nature Publishing Group; Scientific Reports; 11; 1; 23-11-2021; 1-10
2045-2322
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/164586
identifier_str_mv Melzi, Pietro; Tolosana, Ruben; Cecconi, Alberto; Sanz Garcia, Ancor; Ortega, Guillermo José; et al.; Analyzing artificial intelligence systems for the prediction of atrial fibrillation from sinus-rhythm ECGs including demographics and feature visualization; Nature Publishing Group; Scientific Reports; 11; 1; 23-11-2021; 1-10
2045-2322
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-021-02179-1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1846082783192023040
score 13.22299

Publicaciones similares