Predicting Electrical Cardioversion Outcome in Persistent Atrial Fibrillation Through Multiscale Entropy Analysis

Atrial Fibrillation (AF) is the most commonly sustained cardiac arrhythmia and the major cause of cardiovascular morbidity and mortality. Because of its wide availability and initial effectiveness, electrical cardioversion (ECV) is the primary method used for reverting this arrhythmia to normal sinus rhythm (NSR). However, this procedure presents some collateral effects, and barely 80% of the patients prevail in NSR after 1 month. Thus, being able to predict the outcome of ECV before its application is of great interest in clinical practice, so cardiac complications in patients with high probability of early AF recurrence could be prevented. For that purpose, this work characterizes atrial activity (AA) in patients with persistent AF, before ECV, by means of nonlinear multiscale dynamics, particularly composite multiscale entropy (CMSE), and compares its performance with other recently used parameters, such as, dominant atrial frequency, AA's amplitude and sample entropy. The results show that characterizing AA by means of CMSE predicts ECV outcome with an accuracy above 90%, whereas the remaining parameters only forecast correctly about 70% of the analyzed patients. As a conclusion, using complexity techniques at different time scales for AA characterization increases the probability of correctly predicting AF recurrence.