Multiscale sample entropy based on discrete wavelet transform for clinical heart rate variability recognition

Traditional multiscale method uses coarse grained average (CGA) to evaluate sample entropy (SE) parameters in different scales for signal characterization. In this study, we propose to use discrete wavelet transform (DWT) to decompose heart rate variability signals into multiscale sequences for the calculation of SE features for the recognition of congestive heart failure (CHF) and atrial fibrillation (AF) from normal sinus rhythm (NSR). The support vector machine (SVM) is used as the classifier and the capability of the features are justified with leave-one-out cross-validation method. The results demonstrate that the system using multiscale SE features calculated from both CGA and DWT with five dyadic scales outperforms that based on tradition multiscale method using CGA and 20 scales. Compared to the 5-scale CGA method, the proposed 5-scale DWT method achieved 6.7% and 0.77% increases in the recognition rates for CHF and AF, respectively, and resulted in an 8.35% raise in the overall recognition accuracy.