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Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

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Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest

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dc.contributor.author Chicote, Beatriz es_ES
dc.contributor.author Irusta, Unai es_ES
dc.contributor.author Alcaraz, Raul es_ES
dc.contributor.author Rieta, J J es_ES
dc.contributor.author Aramendi, Elisabete es_ES
dc.contributor.author Isasi, Iraia es_ES
dc.contributor.author Alonso, Daniel es_ES
dc.contributor.author Ibarguren, Karlos es_ES
dc.date.accessioned 2017-03-16T15:01:18Z
dc.date.available 2017-03-16T15:01:18Z
dc.date.issued 2016-09
dc.identifier.issn 1099-4300
dc.identifier.uri http://hdl.handle.net/10251/78821
dc.description.abstract Prediction of defibrillation success is of vital importance to guide therapy and improve the survival of patients suffering out-of-hospital cardiac arrest (OHCA). Currently, the most efficient methods to predict shock success are based on the analysis of the electrocardiogram (ECG) during ventricular fibrillation (VF), and recent studies suggest the efficacy of waveform indices that characterize the underlying non-linear dynamics of VF. In this study we introduce, adapt and fully characterize six entropy indices for VF shock outcome prediction, based on the classical definitions of entropy to measure the regularity and predictability of a time series. Data from 163 OHCA patients comprising 419 shocks (107 successful) were used, and the performance of the entropy indices was characterized in terms of embedding dimension (m) and matching tolerance (r). Six classical predictors were also assessed as baseline prediction values. The best prediction results were obtained for fuzzy entropy (FuzzEn) with m = 3 and an amplitude-dependent tolerance of r = 80 μV. This resulted in a balanced sensitivity/specificity of 80.4%/76.9%, which improved by over five points the results obtained for the best classical predictor. These results suggest that a FuzzEn approach for a joint quantification of VF amplitude and its non-linear dynamics may be a promising tool to optimize OHCA treatment. es_ES
dc.description.sponsorship This work received financial support from Spanish Ministerio de Economia y Competitividad, projects TEC2013-31928 and TEC2014-52250-R, and jointly with the Fondo Europeo de Desarrollo Regional (FEDER), project TEC2015-64678-R; from Junta de Comunidades de Castilla La Mancha, project PPII-2014-026-P; and from UPV/EHU through the grant PIF15/190 and through its research unit UFI11/16. en_EN
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation Spanish Ministerio de Economia y Competitividad [TEC2013-31928 ; TEC2014-52250-R] es_ES
dc.relation Fondo Europeo de Desarrollo Regional (FEDER) [TEC2015-64678-R] es_ES
dc.relation Junta de Comunidades de Castilla La Mancha [PPII-2014-026-P] es_ES
dc.relation UPV/EHU [PIF15/190 ; UFI11/16] es_ES
dc.relation.ispartof Entropy es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Ventricular fibrillation es_ES
dc.subject Defibrillation es_ES
dc.subject Shock outcome prediction es_ES
dc.subject Out-of-hospital cardiac arrest es_ES
dc.subject Non-linear dynamics es_ES
dc.subject Entropy measures es_ES
dc.subject Regularity-based entropies es_ES
dc.subject Predictability-based entropies es_ES
dc.subject Fuzzy entropy es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/e18090313
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
dc.contributor.affiliation Universitat Politècnica de València. Grupo de ingeniería en bioseñales e imagen radiológica
dc.description.bibliographicCitation Chicote, B.; Irusta, U.; Alcaraz, R.; Rieta, JJ.; Aramendi, E.; Isasi, I.; Alonso, D.... (2016). Application of Entropy-Based Features to Predict Defibrillation Outcome in Cardiac Arrest. Entropy. 18(9):1-17. doi:10.3390/e18090313 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.3390/e18090313 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu repo/semantics/publishedVersion es_ES
dc.description.volume 18 es_ES
dc.description.issue 9 es_ES
dc.relation.senia 321391 es_ES


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