QRS Complexes Detection by Using the Principal Component Analysis and the Combined Wavelet Entropy for 12-Lead Electrocardiogram Signals

Abstract

A QRS detection method is proposed based on the principal component analysis (PCA) and the combined wavelet entropy for 12-lead electrocardiogram (ECG) signals. Firstly, the base line wander and the high frequency interference are removed for ECG signals. The PCA method is employed to reduce the dimension of filtered signals. Then, the quasi-period sorting method is proposed to reorder principal components (PCs), which may help the following combined wavelet entropy based method detecting the QRS complex in the lower sorted PCs easily. The proposed method is evaluated against the standard St. Petersburg institute of cardiological technics 12-lead arrhythmia database with other two different QRS detection methods for the single-lead ECG signal and two-lead ECG signals respectively. Experimental results show that the proposed method gives the best overall performance. It achieves an average detection rate of 99.980%, a sensitivity of 99.997%, and a positive prediction of 99.987%.