Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms

This work introduces for the first time the application of wavelet entropy (WE) to detect episodes of the most common cardiac arrhythmia, atrial fibrillation (AF), automatically from the electrocardiogram (ECG). Given that AF is often asymptomatic and usually presents very brief initial episodes, it...

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Autores: Ródenas García, Juan, García Teruel, Manuel, Alcaraz Martínez, Raúl, Rieta Ibáñez, José Joaquín
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/12735
Acceso en línea:http://hdl.handle.net/10578/12735
Access Level:acceso abierto
Palabra clave:Atrial fibrillation
Electrocardiogram
Wavelet entropy
Wavelet transform
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spelling Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiogramsRódenas García, JuanGarcía Teruel, ManuelAlcaraz Martínez, RaúlRieta Ibáñez, José JoaquínAtrial fibrillationElectrocardiogramWavelet entropyWavelet transformThis work introduces for the first time the application of wavelet entropy (WE) to detect episodes of the most common cardiac arrhythmia, atrial fibrillation (AF), automatically from the electrocardiogram (ECG). Given that AF is often asymptomatic and usually presents very brief initial episodes, its early automatic detection is clinically relevant to improve AF treatment and prevent risks for the patients. After discarding noisy TQ intervals from the ECG, the WE has been computed over the median TQ segment obtained from the 10 previous noise-free beats under study. In this way, the P-waves or the fibrillatory waves present in the recording were highlighted or attenuated, respectively, thus enabling the patient’s rhythm identification (sinus rhythm or AF). Results provided a discriminant ability of about 95%, which is comparable to previous works. However, in contrast to most of them, which are mainly based on quantifying RR series variability, the proposed algorithm is able to deal with patients under rate-control therapy or with a reduced heart rate variability during AF. Additionally, it also presents interesting properties, such as the lowest delay in detecting AF or sinus rhythm, the ability to detect episodes as brief as five beats in length or its integration facilities under real-time beat-by-beat ECG monitoring systems. Consequently, this tool may help clinicians in the automatic detection of a wide variety of AF episodes, thus gaining further knowledge about the mechanisms initiating this arrhythmiaMDPI201720172015info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttp://hdl.handle.net/10578/12735reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/127352026-05-27T07:36:41Z
dc.title.none.fl_str_mv Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms
title Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms
spellingShingle Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms
Ródenas García, Juan
Atrial fibrillation
Electrocardiogram
Wavelet entropy
Wavelet transform
title_short Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms
title_full Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms
title_fullStr Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms
title_full_unstemmed Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms
title_sort Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms
dc.creator.none.fl_str_mv Ródenas García, Juan
García Teruel, Manuel
Alcaraz Martínez, Raúl
Rieta Ibáñez, José Joaquín
author Ródenas García, Juan
author_facet Ródenas García, Juan
García Teruel, Manuel
Alcaraz Martínez, Raúl
Rieta Ibáñez, José Joaquín
author_role author
author2 García Teruel, Manuel
Alcaraz Martínez, Raúl
Rieta Ibáñez, José Joaquín
author2_role author
author
author
dc.subject.none.fl_str_mv Atrial fibrillation
Electrocardiogram
Wavelet entropy
Wavelet transform
topic Atrial fibrillation
Electrocardiogram
Wavelet entropy
Wavelet transform
description This work introduces for the first time the application of wavelet entropy (WE) to detect episodes of the most common cardiac arrhythmia, atrial fibrillation (AF), automatically from the electrocardiogram (ECG). Given that AF is often asymptomatic and usually presents very brief initial episodes, its early automatic detection is clinically relevant to improve AF treatment and prevent risks for the patients. After discarding noisy TQ intervals from the ECG, the WE has been computed over the median TQ segment obtained from the 10 previous noise-free beats under study. In this way, the P-waves or the fibrillatory waves present in the recording were highlighted or attenuated, respectively, thus enabling the patient’s rhythm identification (sinus rhythm or AF). Results provided a discriminant ability of about 95%, which is comparable to previous works. However, in contrast to most of them, which are mainly based on quantifying RR series variability, the proposed algorithm is able to deal with patients under rate-control therapy or with a reduced heart rate variability during AF. Additionally, it also presents interesting properties, such as the lowest delay in detecting AF or sinus rhythm, the ability to detect episodes as brief as five beats in length or its integration facilities under real-time beat-by-beat ECG monitoring systems. Consequently, this tool may help clinicians in the automatic detection of a wide variety of AF episodes, thus gaining further knowledge about the mechanisms initiating this arrhythmia
publishDate 2015
dc.date.none.fl_str_mv 2015
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10578/12735
url http://hdl.handle.net/10578/12735
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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