Session PB9.4

Optimal Basis Function Study in Wavelet Sample Entropy for Electrical Cardioversion Outcome Prediction of Persistent Atrial Fibrillation

R Alcaraz*, JJ Rieta

Universidad Politecnica de Valencia
Valencia, Spain

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 estimated AF organization based on the combination of Wavelet decomposition and non-linear regularity metrics, such as Sample Entropy (SampEn). To this respect, the regularity of the wavelet coefficient vector associated to the seventh discrete scale (4-8 Hz frequency range) of the atrial activity (AA) signal was estimated with SampEn. In patients with a more organized AF, the arrhythmia recurrence likelihood was lower after ECV.
However, WSE was 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. Concretely, all the different functions from Haar, Daubechies, Coiflet, Biorthogonal, Reverse Biorthogonal, and Symlet wavelet families were tested. Only orthogonal families were studied because only in an orthogonal basis can any signal be uniquely decomposed and the decomposition be inverted without loosing information.
Forty PAF patients undergoing ECV and followed during four weeks were used for the proposed comparative analysis. After ECV, in 21 patients (60%) normal sinus rhythm (NSR) duration was below one month, whereas the other 14 patients (40%) remained in NSR after the first month. To compare the different wavelet functions performance, sensitivity and specificity were computed making use of the ROC curve.
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.

(Abstract Control Number: 37)