A real-time simulated QRS detection system constructed using wavelet filtering technique

Electrocardiography (ECG) signals analysis is a very important step in the detection of some of the electrophysiological abnormalities that distinguish patients with and without sustained late potential problems. Unfortunately, approaches developed so far, both in time and frequency domain of the QRS complex suffer from a relatively low positive-predictive accuracy. This underscores the requirement to improve methods. In this study, we investigated the applications of wavelet filter banks. These are then applied to the problem of distinguishing patients with and without late potential. In the course of the work, we studied various accuracy scenarios using basic filtering and wavelet filtering techniques. We developed a new approach called subframe approximation and applied this approach to the wavelet filters. The real improvement is achieved in the wavelet filtering method and discrete wavelet analysis