Machine learning based detection of T-wave alternans in real ambulatory conditions

Background and objective T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challeng...

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Detalhes bibliográficos
Autores: Pascual Sánchez, Lidia|||0000-0001-6042-6300, Goya Esteban, Rebeca, Cruz Roldán, Fernando|||0000-0001-6843-5199, Hernández Madrid, Antonio, Blanco Velasco, Manuel|||0000-0001-6593-1517
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/61283
Acesso em linha:http://hdl.handle.net/10017/61283
https://dx.doi.org/10.1016/j.cmpb.2024.108157
Access Level:acceso abierto
Palavra-chave:Machine learning (ML)
Spectral method (SM)
Modified moving average method (MMA)
Time method (TM)
Cross validation (CV)
RepolarizationT-wave alternans (TWA)
Electrocardiogram (ECG)
Telecomunicaciones
Telecommunication
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spelling Machine learning based detection of T-wave alternans in real ambulatory conditionsPascual Sánchez, Lidia|||0000-0001-6042-6300Goya Esteban, RebecaCruz Roldán, Fernando|||0000-0001-6843-5199Hernández Madrid, AntonioBlanco Velasco, Manuel|||0000-0001-6593-1517Machine learning (ML)Spectral method (SM)Modified moving average method (MMA)Time method (TM)Cross validation (CV)RepolarizationT-wave alternans (TWA)Electrocardiogram (ECG)TelecomunicacionesTelecommunicationBackground and objective T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds. Methods In this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K–nearest–neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper–parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events. Results We train ML methods to detect a wide variety of alternant voltage from 20 to 100 μV, i.e., ranging from non–visible micro–alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods. Conclusions We propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores.Universidad de AlcaláAgencia Estatal de InvestigaciónElsevier20242024-04-03journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10017/61283https://dx.doi.org/10.1016/j.cmpb.2024.108157reponame:e_Buah Biblioteca Digital Universidad de Alcaláinstname:Universidad de Alcalá (UAH)InglésengUAH Not available EPU-INV%2F2020%2F002UAH Not available PIUAH23%2FIA-014Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-140786NB-C32 DISEÑO DE UN PATRON ORO Y METODOS INTERPRETABLES BASADOS APRENDIZAJE AUTOMATICO PARA LA CARACTERIZACION ALTERNANCIAS DE LA ONDA T EN REGISTROS AMBULATORIOSopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:ebuah.uah.es:10017/612832026-06-18T11:13:07Z
dc.title.none.fl_str_mv Machine learning based detection of T-wave alternans in real ambulatory conditions
title Machine learning based detection of T-wave alternans in real ambulatory conditions
spellingShingle Machine learning based detection of T-wave alternans in real ambulatory conditions
Pascual Sánchez, Lidia|||0000-0001-6042-6300
Machine learning (ML)
Spectral method (SM)
Modified moving average method (MMA)
Time method (TM)
Cross validation (CV)
RepolarizationT-wave alternans (TWA)
Electrocardiogram (ECG)
Telecomunicaciones
Telecommunication
title_short Machine learning based detection of T-wave alternans in real ambulatory conditions
title_full Machine learning based detection of T-wave alternans in real ambulatory conditions
title_fullStr Machine learning based detection of T-wave alternans in real ambulatory conditions
title_full_unstemmed Machine learning based detection of T-wave alternans in real ambulatory conditions
title_sort Machine learning based detection of T-wave alternans in real ambulatory conditions
dc.creator.none.fl_str_mv Pascual Sánchez, Lidia|||0000-0001-6042-6300
Goya Esteban, Rebeca
Cruz Roldán, Fernando|||0000-0001-6843-5199
Hernández Madrid, Antonio
Blanco Velasco, Manuel|||0000-0001-6593-1517
author Pascual Sánchez, Lidia|||0000-0001-6042-6300
author_facet Pascual Sánchez, Lidia|||0000-0001-6042-6300
Goya Esteban, Rebeca
Cruz Roldán, Fernando|||0000-0001-6843-5199
Hernández Madrid, Antonio
Blanco Velasco, Manuel|||0000-0001-6593-1517
author_role author
author2 Goya Esteban, Rebeca
Cruz Roldán, Fernando|||0000-0001-6843-5199
Hernández Madrid, Antonio
Blanco Velasco, Manuel|||0000-0001-6593-1517
author2_role author
author
author
author
dc.subject.none.fl_str_mv Machine learning (ML)
Spectral method (SM)
Modified moving average method (MMA)
Time method (TM)
Cross validation (CV)
RepolarizationT-wave alternans (TWA)
Electrocardiogram (ECG)
Telecomunicaciones
Telecommunication
topic Machine learning (ML)
Spectral method (SM)
Modified moving average method (MMA)
Time method (TM)
Cross validation (CV)
RepolarizationT-wave alternans (TWA)
Electrocardiogram (ECG)
Telecomunicaciones
Telecommunication
description Background and objective T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds. Methods In this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K–nearest–neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper–parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events. Results We train ML methods to detect a wide variety of alternant voltage from 20 to 100 μV, i.e., ranging from non–visible micro–alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods. Conclusions We propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-04-03
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10017/61283
https://dx.doi.org/10.1016/j.cmpb.2024.108157
url http://hdl.handle.net/10017/61283
https://dx.doi.org/10.1016/j.cmpb.2024.108157
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv UAH Not available EPU-INV%2F2020%2F002
UAH Not available PIUAH23%2FIA-014
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-140786NB-C32 DISEÑO DE UN PATRON ORO Y METODOS INTERPRETABLES BASADOS APRENDIZAJE AUTOMATICO PARA LA CARACTERIZACION ALTERNANCIAS DE LA ONDA T EN REGISTROS AMBULATORIOS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
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:e_Buah Biblioteca Digital Universidad de Alcalá
instname:Universidad de Alcalá (UAH)
instname_str Universidad de Alcalá (UAH)
reponame_str e_Buah Biblioteca Digital Universidad de Alcalá
collection e_Buah Biblioteca Digital Universidad de Alcalá
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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