Abstract
Several clinical factors have been studied to predict atrial fibrillation (AF) recurrence after electrical cardioversion (ECV) with limited predictive value. In this work, several time-frequency parameters from the fibrillatory (f) waves characterization were studied to improve ECV outcome prediction at mid follow-up. By analyzing ECV outcome one month after the procedure of 63 persistent AF patients, f waves power (fWP) presented the highest predictive accuracy of 82.5%, whereas f waves organization, computed by sample entropy (SampEn), provided a 79.4%. Other analyzed features revealed accuracies lower than 70%. A stepwise discriminant analysis provided a model based on fWP and SampEn with 90.5% of accuracy. Moreover, a thorough analysis of the results allowed the outline of possible associations between these two features and the concomitant status of atrial remodeling. As a consequence, the information provided by advanced signal processing methodologies could be more effective in the prediction of long-standing AF early recurrences than previously analyzed clinical parameters.
