Multidimensional Characterization of the Atrial Activity to Predict Electrical Cardioversion Outcome of Persistent Atrial Fibrillation

dc.contributor.authorCirugeda, Eva Maria
dc.contributor.authorCalero, Sofía
dc.contributor.authorPlancha, Eva
dc.contributor.authorEnero, José
dc.contributor.authorRieta, Jose Joaquín
dc.contributor.authorAlcaraz, Raúl
dc.date.accessioned2025-07-31T10:58:47Z
dc.date.available2025-07-31T10:58:47Z
dc.date.issued2020-09-16
dc.description.abstractEuropean Society of Cardiology guidelines recommend electrical cardioversion (ECV) as a rhythm control strategy in persistent atrial fibrillation (AF). Although ECV initially restores sinus rhythm (SR) in almost every patient, mid- and long-term AF recurrence rates are high, so that additional research is needed to anticipate ECV outcome and rationalize the management of AF patients. Although indices characterizing fibrillatory (f-) waves from surface lead V1, such as dominant frequency (DF), amplitude (FWA), and entropy, have reported good results, they discard the spatial information from the remaining leads. Hence, this work explores whether a multidimensional characterization approach of these parameters can improve ECV outcome prediction. The obtained results have shown that multidimensional FWA reported more balanced values of sensitivity and specificity, although the discriminant ability was similar in both cases. For DF, a similar outcome was also obtained. In contrast, multivariate entropy overcome discriminant ability of its univariate version by 5%, rightly anticipating result in more than 80% of ECV cases. Therefore, multidimensional entropy analysis seems to be able to quantify novel dynamics in the f-waves, which lead to a better ECV outcome prediction.
dc.identifier.citationCirugeda, E. M., Calero, S., Plancha, E., Enero, J., Rieta, J. J., & Alcaraz, R. (2020). Multidimensional characterization of the atrial activity to predict electrical cardioversion outcome of persistent atrial fibrillation. In Computing in Cardiology, 47, 1–4. https://doi.org/10.22489/CinC.2020.37
dc.identifier.doi10.22489/CinC.2020.377
dc.identifier.issn2325-887X
dc.identifier.urihttps://hdl.handle.net/10115/97297
dc.language.isoen
dc.publisherComputing in Cardiology
dc.rightsAttribution 4.0 Internationalen
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectElectrical Cardioversion
dc.subjectECV
dc.subjectAtrial Fibrillation
dc.subjectAF
dc.subject12-lead ECG
dc.subjectMultidimensional analysis
dc.subjectSample Entropy
dc.subjectdominant atrial frequency
dc.subjectfibrillatory waves amplitude
dc.subjectatrial activity
dc.titleMultidimensional Characterization of the Atrial Activity to Predict Electrical Cardioversion Outcome of Persistent Atrial Fibrillation
dc.typeArticle

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