An Approach Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

This paper highlights a new method for ECG segmentation based on the combination of two mathematical techniques namely the wavelet transform (WT) and hidden Markov models (HMM). In this method, we first localize edges in the ECG by wavelet coefficients, then, features extracted from the edges serve as input for the HMM. This new approach was tested and evaluated on the manually annotated database QT database (QTDB), which is regarded as a very important benchmark for ECG analysis. We obtained a sensitivity S_e= 99,40% for QRS detection and a sensitivity S_e= 94,65% for T wave detection.