Abstract: QRS complex detecting algorithm was core of ECG auto-diagnosis method, heart rate variability analysis and deeply influences cardiac cycle division for signal compression. However, ECG signals collected by noninvasive surface electrodes were confused by several kinds of noise and its waveform variation was the main reasons for the hard realization of 100% detection accruracy. QRS complex detecting algorithms based on mixed methods were studied. This study proposed a QRS complex detecting algorithm based on wavelet transform and multi-resolution mathematical morphological decomposition (WMR algorithm). This algorithm possessed superiorities in R peak detection of the two methods. Moreover, a pre-processing method based on lifting scheme constructing multi-resolution morphological decomposition was adopted to reduce noise affection. And an efficient R peak search-back algorithm was employed to reduce the False Positives (FP) and False Negatives (FN). According to simulation results in MIT-BIH Arrhythmia Database, QRS detection accuracy was over 99.8%.