Centralized Wavelet Multiresolution for Exact Translation Invariant Processing of ECG Signals

Abstract

Dyadic wavelet transform is useful in analyzing electrocardiogram (ECG) signals due to its fast computation and its multiresolution ability. In order to improve the feature extraction performance of dyadic wavelet transform, a new construction example of centralized multiresolution (CMR) is proposed. The proposed CMR example consists of two elements, namely, a dyadic part and a non-dyadic part. The dyadic part, based on the maximal overlap second generation wavelet packet transform (SGWPT), generates dyadic wavelet packets. The non-dyadic part engenders ensemble wavelet packets by postprocessing on the dyadic part. The produced wavelet packets and ensemble wavelet packets are combined to realize continued spectral refinement around fixed central analysis frequencies. Numerical simulation and a case study of ECG signal decomposition are utilized to validate the enhancements of the proposed CMR example. The processing results of the CMR example are compared with those of the dual tree complex wavelet transform and the conventional SGWPT. It is validated this CMR example achieves better feature extraction performances due to the presence of the exact translation invariance property.