Cirugeda Roldan, Eva MaríaCalero, SofíaHidalgo , Víctor ManuelEneró, JoséRieta, José JoaquínAlcaraz, Raúl2025-08-012025-08-012020-09-16E. M. Cirugeda, S. Calero, V. M. Hidalgo, J. Enero, J. J. Rieta and R. Alcaraz, "Refined Multiscale Entropy Predicts Early Failure in Electrical Cardioversion of Atrial Fibrillation," 2020 Computing in Cardiology, Rimini, Italy, 2020, pp. 1-4, doi: 10.22489/CinC.2020.369.2325-887Xhttps://hdl.handle.net/10115/97357Electrical cardioversion (ECV) is a well-established strategy for atrial fibrillation (AF) management. Despite its high initial effectiveness, a high relapsing rate is also found. Hence, identification of patients at high risk of early AF recurrence is crucial for a rationale therapeutic strategy. For that purpose, a set of indices characterizing fibrillatory (f-) waves have been proposed, but they have not considered nonlinear dynamics present at different timescales within the cardiovascular system. This work thus explores whether a multiscale entropy (MSE) analysis of the f-waves can improve preoperative predictions of ECV outcome. Thus, two MSE approaches were considered, i.e., traditional MSE and a refined version (RMSE). Both algorithms were applied to the main f-waves component extracted from lead V1 and entropy values were computed for the first 20 time-scales. As a reference, dominant frequency (DF) and f-wave amplitude (FWA) were also computed. A total of 70 patients were analyzed, and all parameters but FWA showed statistically significant differences between those relapsing to AF and maintaining sinus rhythm during a follow-up of 4 weeks. RMSE reported the best results for the scale 19, improving predictive ability up to an 8% with respect to DAF and FWA. Consequently, investigation of nonlinear dynamics at large time-scales can provide useful insights able to improve predictions of ECV failure.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Atrial ActivityAtrial fribrillationnonlinear analysiselectrical cardioversionrefined multiscale entropymultiscale entropyArticle10.22489/CinC.2020.369info:eu-repo/semantics/openAccess