Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study

Objective: Artificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR...

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Autores: Melzi, Pietro, Vera Rodríguez, Rubén, Tolosana Moranchel, Rubén, Sanz-García, Ancor, Cecconi, Alberto, Ortega, Guillermo J., Jiménez-Borreguero, Luis Jesús
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/709461
Acceso en línea:http://hdl.handle.net/10486/709461
https://dx.doi.org/10.1016/j.irbm.2023.100811
Access Level:acceso abierto
Palabra clave:ECG
Healthcare
Artificial Intelligence
Atrial Fibrillation
Deep Learning
Electrónica
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spelling Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal studyMelzi, PietroVera Rodríguez, RubénTolosana Moranchel, RubénSanz-García, AncorCecconi, AlbertoOrtega, Guillermo J.Jiménez-Borreguero, Luis JesúsECGHealthcareArtificial IntelligenceAtrial FibrillationDeep LearningElectrónicaObjective: Artificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures. Methods: We designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics. Results: After balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09). Conclusion: This study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stabilityEuropean Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No860813 – TReSPAsS-ETNTRESPASS-ETNElsevierDepartamento de Tecnología Electrónica y de las ComunicacionesEscuela Politécnica Superior20232023-10-27research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/709461https://dx.doi.org/10.1016/j.irbm.2023.100811reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengEuropean Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 860813open accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7094612026-06-23T12:46:27Z
dc.title.none.fl_str_mv Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study
title Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study
spellingShingle Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study
Melzi, Pietro
ECG
Healthcare
Artificial Intelligence
Atrial Fibrillation
Deep Learning
Electrónica
title_short Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study
title_full Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study
title_fullStr Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study
title_full_unstemmed Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study
title_sort Prediction of atrial fibrillation from sinus-rhythm electrocardiograms based on deep neural networks: Analysis of time intervals and longitudinal study
dc.creator.none.fl_str_mv Melzi, Pietro
Vera Rodríguez, Rubén
Tolosana Moranchel, Rubén
Sanz-García, Ancor
Cecconi, Alberto
Ortega, Guillermo J.
Jiménez-Borreguero, Luis Jesús
author Melzi, Pietro
author_facet Melzi, Pietro
Vera Rodríguez, Rubén
Tolosana Moranchel, Rubén
Sanz-García, Ancor
Cecconi, Alberto
Ortega, Guillermo J.
Jiménez-Borreguero, Luis Jesús
author_role author
author2 Vera Rodríguez, Rubén
Tolosana Moranchel, Rubén
Sanz-García, Ancor
Cecconi, Alberto
Ortega, Guillermo J.
Jiménez-Borreguero, Luis Jesús
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Tecnología Electrónica y de las Comunicaciones
Escuela Politécnica Superior
dc.subject.none.fl_str_mv ECG
Healthcare
Artificial Intelligence
Atrial Fibrillation
Deep Learning
Electrónica
topic ECG
Healthcare
Artificial Intelligence
Atrial Fibrillation
Deep Learning
Electrónica
description Objective: Artificial Intelligence (AI) in electrocardiogram (ECG) analysis helps to identify persons at risk of developing atrial fibrillation (AF) and reduces the risk for severe complications. Our aim is to investigate the performance of AI-based methods predicting future AF from sinus rhythm (SR) ECGs, according to different characteristics of patients, time intervals for prediction, and longitudinal measures. Methods: We designed a retrospective, prognostic study to predict AF occurrence in patients from 12-lead SR ECGs. We classified patients in two groups, according to their ECGs: 3,761 developed AF and 22,896 presented only SR ECGs. We assessed the impact of age on the overall performance of deep neural network (DNN)-based systems, which consist in a variation of Residual Networks for time series. Then, we analysed how much in advance our system can predict AF from SR ECGs and the performance for different categories of patients with AUC and other metrics. Results: After balancing the age distribution between the two groups of patients, our model achieves AUC of 0.79 (0.72-0.86) without additional constraints, 0.83 (0.76-0.89) for ECGs recorded in the last six months before AF, and 0.87 (0.81-0.93) for patients with stable AF risk measures over time, with sensitivity of 90.62% (80.70-96.48) and diagnostic odd ratio of 20.49 (8.56-49.09). Conclusion: This study shows the ability of DNNs to predict new onsets of AF from SR ECGs, with the best performance achieved for patients with stable AF risk score over time. The introduction of this time-based score opens new possibilities for AF prediction, thanks to the analysis of long-span time intervals and score stability
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-10-27
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/709461
https://dx.doi.org/10.1016/j.irbm.2023.100811
url http://hdl.handle.net/10486/709461
https://dx.doi.org/10.1016/j.irbm.2023.100811
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/501100000780 Horizon 2020 Framework Programme 860813
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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