Detecting QRS Complexes of Two-Channel ECG Signals by Using Combined Wavelet Entropy

A QRS detection method based on the combined wavelet entropy (CWS) is proposed for two-channel ECG signals. The raw ECG signals from two channels are firstly transformed by the continuous wavelet transform within a selected frequency interval, which can effectively suppress the interference and only focus on the QRS component. The wavelet entropy (WS) based method is employed to analyze the ECG signal from channel 1 based on an automated threshold determination. This method may fail when the current RR interval is larger than 1.5 times the average RR interval. Here, another WS has to be calculated for ECG signal from channel 2, and the CWS is defined as the sum of WS from two channel signals. The time evolution of the CWS is calculated, which can enhance detection results by using the QRS information coming from both channels. Experimental results indicate that this proposed method achieves an average detection rate of 99.84%, a sensitivity of 99.91%, and a positive prediction of 99.93% over both two leads of ECG signals from the MIT-BIH arrhythmia database, which outperform some published results of other detection algorithms.