QRS Complex Detection Using Combination of Mexican-hat Wavelet and Complex Morlet Wavelet

QRS complex detection is usually the most important step for automated electrocardiogram (ECG) analysis. In this paper, we present a new approach of QRS complex detection. The Mexican-hat wavelet and complex Morlet wavelet are used to transform the ECG signal, and according to the trait that the modulus maxima of the two wavelet coefficients above correspond with R peaks of ECG signal, a detector unit of R waves which is based on the jump of modulus maxima sequence in wavelet coefficient is proposed. Traditional wavelet based QRS complex detection methods employs only one kind of wavelet to perform the transformation of ECG, whereas the proposed method use two kind of wavelet at the same time, and then using the proposed detector unit in the linear combination of the two wavelet coefficients to detect R waves. In this processing, group search optimizer is introduced to get some best thresholds. Experiment results show that our QRS complex detection achieved a detection sensitive of 99.71% and positive prediction of 99.53% according to the MIT-BIH database. A combination of two wavelets is a simple and efficient way to improve the performance of wavelet based QRS complex detection methods.