Using Hidden Markov Models for ECG Characterisation
- Krimi Samar, Manar Tunis
In this paper we develop a new approach to ECG analysis, combining Pitch Synchronous Wavelet Transform (PSWT) and Hidden Semi-Markov Model (HSMM) for tracking the typical ECG cycle. The combination of these two techniques was examined in a way that the PSWT of an ECG signal was an input for the HSMM. This approach was tested and evaluated on the manually annotated QT database. Experimental results show the accuracy of the proposed technique for all corrupted ECG tested reaching a sensitivity Se=99,95% for QRS detection and Se=97,79% for T detection.