Supervised ECG Delineation Using the Wavelet Transform and Hidden Markov Models

Clinical monitoring and pharmaceutical phase-one studies require feature extraction from the ECG signal in order to evaluate the state of a patient's heart. Automatic annotation of the characteristic ECG waveforms (or so-called delineation) is therefore of great interest. Hidden Markov Models (HMM) coupled to wavelet transforms (WT) of the ECG signal offer significant improvements over standard heuristic delineation methods. Nevertheless, the choice of the WT parameters remains empirical rather than data-driven. In these conditions, suboptimal parameters for the WT may degrade the results very much. In this paper, an algorithm for the optimal selection of the WT parameter values is introduced. The model complexity is strongly reduced and the algorithm can adapt itself to the specificities of each ECG signal while avoiding redundancy, noise and useless information. Evaluation on recordings from the public MITQT database leads to results higher than with state of the art methods.