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Wavelet Sample Entropy Optimization through Optimal Mother Function Selection for Atrial Fibrillation Analysis

Wavelet Sample Entropy (WSE) has been previously introduced as a successful methodology to predict electrical cardioversion (ECV) outcome of persistent atrial fibrillation (AF). The method estimates AF organization based on the combination of Wavelet decomposition and non-linear regularity metrics, such as Sample Entropy (SampEn). However, WSE has been only computed by applying a specific wavelet function, such as the fourth-order biorthogonal wavelet. In the present work, with the objective of improving WSE robustness and its diagnostic ability in ECV outcome prediction, several orthogonal wavelet families were tested, and their performances were compared. Results indicated that, for all the functions of the same wavelet family, the same sensitivity and specificity were obtained. Additionally, all the wavelet families reached the same diagnostic ability (80.95% sensitivity and 85.71% specificity), being the same patients incorrectly classified by all the families. These results suggest that any wavelet family could be indistinctly used to estimate successfully AF organization with the WSE methodology. As a consequence, the design of a customized wavelet function adapted to the specific characteristics of AA would not improve the WSE diagnostic ability in the prediction of ECV outcome in AF.