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Application of wavelet entropy to predict atrial fibrillation progression from the surface ECG

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Application of wavelet entropy to predict atrial fibrillation progression from the surface ECG

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Nombre: R. Alcaraz;Rieta - ...
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dc.contributor.author Alcaraz, Raúl es_ES
dc.contributor.author Rieta Ibañez, José Joaquín es_ES
dc.date.accessioned 2014-12-11T13:06:06Z
dc.date.available 2014-12-11T13:06:06Z
dc.date.issued 2012
dc.identifier.issn 1748-670X
dc.identifier.uri http://hdl.handle.net/10251/45360
dc.description.abstract Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice, thus, being the subject of intensive research both in medicine and engineering. Wavelet Entropy (WE) is a measure of the disorder degree of a specific phenomena in both time and frequency domains, allowing to reveal underlying dynamical processes out of sight for other methods. The present work introduces two different WE applications to the electrocardiogram (ECG) of patients in AF. The first application predicts the spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the electrical cardioversion (ECV) outcome in persistent AF patients. In both applications, WE was used with the objective of assessing the atrial fibrillatory ( f ) waves organization. Structural changes into the f waves reflect the atrial activity organization variation, and this fact can be used to predict AF progression. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity, and accuracy were 95.38%, 91.67%, and 93.60%, respectively. On the other hand, for ECV outcome prediction, 85.24% sensitivity, 81.82% specificity, and 84.05% accuracy were obtained. These results turn WE as the highest single predictor of spontaneous PAF termination and ECV outcome, thus being a promising tool to characterize non-invasive AF signals. es_ES
dc.description.sponsorship This work was supported by the projects TEC2010-20633 from the Spanish Ministry of Science and Innovation and PPII11-0194-8121 and PII1C09-0036-3237 from Junta de Comunidades de Castilla-La Mancha. en_EN
dc.language Inglés es_ES
dc.publisher Hindawi Publishing Corporation es_ES
dc.relation.ispartof Computational and Mathematical Methods in Medicine es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Atrial fibrillation es_ES
dc.subject ECG es_ES
dc.subject Signal processing es_ES
dc.subject Wavelet transform es_ES
dc.subject Sample Entropy es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Application of wavelet entropy to predict atrial fibrillation progression from the surface ECG es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1155/2012/245213
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TEC2010-20633/ES/DESARROLLO Y APLICACION DE ESTIMADORES AVANZADOS DE ORGANIZACION PARA LA CLASIFICACION TERAPEUTICA Y EL SEGUIMIENTO DE PACIENTES CON FIBRILACION AURICULAR/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Junta de Comunidades de Castilla-La Mancha//PII1C09-0036-3237/ES/Predicción De Riesgo De Muerte Súbita Tras Infarto De Miocardio Mediante Técnicas Avanzadas De Procesado Digital De Señal/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Junta de Comunidades de Castilla-La Mancha//PPII11-0194-8121]/ES/PPII11-0194-8121]/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Alcaraz, R.; Rieta Ibañez, JJ. (2012). Application of wavelet entropy to predict atrial fibrillation progression from the surface ECG. Computational and Mathematical Methods in Medicine. 2012(245213):1-9. https://doi.org/10.1155/2012/245213 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1155/2012/245213 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 9 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 2012 es_ES
dc.description.issue 245213 es_ES
dc.relation.senia 239045
dc.identifier.pmid 23056146 en_EN
dc.identifier.pmcid PMC3463933 en_EN
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
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