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dc.contributor.author | Cirugeda, Eva M. | es_ES |
dc.contributor.author | Calero, Sofía | es_ES |
dc.contributor.author | Plancha, Eva | es_ES |
dc.contributor.author | Enero, José | es_ES |
dc.contributor.author | Rieta, J J | es_ES |
dc.contributor.author | Alcaraz, Raúl | es_ES |
dc.date.accessioned | 2022-01-18T08:11:45Z | |
dc.date.available | 2022-01-18T08:11:45Z | |
dc.date.issued | 2020-10-30 | es_ES |
dc.identifier.isbn | 978-1-7281-8803-4 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/179800 | |
dc.description | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | es_ES |
dc.description.abstract | [EN] The European Society of Cardiology guidelines recommend electrical cardioversion (ECV) as a rhythm control strategy in persistent atrial fibrillation (AF). Although being able to initially restore sinus rhythm in most patients, mid- and long-term AF recurrence rates are high. In this context, anticipation of ECV outcome is interesting to rationalize the management of AF patients. To this end, several parameters have been recently proposed for atrial activity (AA) characterization, such as fibrillatory wave amplitude (FWA), dominant frequency (DF) and sample entropy (SEn). These indices have revealed promising results, but have been mainly computed from lead V1, thus discarding spatial information from the remaining leads. Hence, this work explores whether a multidimensional extension of these parameters can improve ECV outcome prediction. Results showed that multidimensional parameters provided more balanced values of sensitivity and specificity than unidimensional ones. While FWA and DF showed similar discriminant ability among both approaches, multivariate SEn improved the discriminant ability of its univariate version by 5%, thus predicting 80% of the ECV procedures correctly. Consequently, whereas multivariate extension of linear parameters did not reveal new predictive information, multidimensional entropy analysis was able to quantify novel AA dynamics, which have been helpful in improving ECV outcome prediction. | es_ES |
dc.description.sponsorship | This research was funded by projects DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from "Junta de Castilla la Mancha" and AICO/2019/036 from "Generalitat Valenciana". | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | IEEE | es_ES |
dc.relation.ispartof | 2020 E-Health and Bioengineering Conference (EHB) | es_ES |
dc.relation.ispartofseries | E-Health and Bioengineering Conference | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Atrial fibrillation | es_ES |
dc.subject | Electrical cardioversion | es_ES |
dc.subject | Atrial activity | es_ES |
dc.subject | Sample entropy | es_ES |
dc.subject | Mutivariate sample entropy | es_ES |
dc.subject.classification | TECNOLOGIA ELECTRONICA | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.title | Multidimensional Fibrillatory Waves Analysis for Improved Electrical Cardioversion Outcome Prediction in Persistent Atrial Fibrillation | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.identifier.doi | 10.1109/EHB50910.2020.9280226 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-83952-C3-1-R/ES/ESTUDIO MULTICENTRICO PARA LA EVALUACION DEL SUSTRATO ARRITMOGENICO EN PACIENTES CON FIBRILACION AURICULAR. APLICACION A LA ABLACION POR CATETER/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/JCCM//SBPLY%2F17%2F180501%2F000411//Caracterización del sustrato auricular mediante análisis de señal como herramienta de asistencia procedimental en ablación por catéter de fibrilación auricular/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement///AICO%2F2019%2F036//METODOS DE DIAGNOSTICO Y TERAPIA PERSONALIZADA EN ABLACION POR CATETER DE ARRITMIAS CARDIACAS/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto Universitario Mixto Tecnológico de Informática - Institut Universitari Mixt Tecnològic d'Informàtica | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica | es_ES |
dc.description.bibliographicCitation | Cirugeda, EM.; Calero, S.; Plancha, E.; Enero, J.; Rieta, JJ.; Alcaraz, R. (2020). Multidimensional Fibrillatory Waves Analysis for Improved Electrical Cardioversion Outcome Prediction in Persistent Atrial Fibrillation. IEEE. 1-4. https://doi.org/10.1109/EHB50910.2020.9280226 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.conferencename | 8th International Conference on e-Health and Bioengineering (EHB 2020) | es_ES |
dc.relation.conferencedate | Octubre 29-30,2020 | es_ES |
dc.relation.conferenceplace | Online | es_ES |
dc.relation.publisherversion | https://doi.org/10.1109/EHB50910.2020.9280226 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 4 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | S\433204 | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Junta de Comunidades de Castilla-La Mancha | es_ES |