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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/164586
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:40:45Zoai: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:34982025-09-29 09:40:45.693CONICET 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 |
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1844613289634430976 |
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13.070432 |