Using Hidden Markov Models for ECG Characterisation

Abstract

Recent research on biomedical signal processing especially ECG analysis mostly focused on the use of Hidden Markov Models (HMM). The general aim of any signal segmentation method is to partition a given signal into consecutive regions of interest. In the context of the ECG then, the role of segmentation is to determine as accurately as possible the onset and offset boundaries, as well as the peak locations, of the various waveform features, such that the ECG interval measurements may be computed automatically and the study of waveform patterns will be facilitated (Sayadi & Shamsollahi, 2009). Ad hoc algorithms have been developed in order to help cardiologists to segment large amounts of ECGs. But these algorithms do not provide a precise segmentation, and repetitive corrections have to be made. Wavelet parametrisation is known to highlight discontinuities in the signal, and has proven to give good results for ECG segmentation (Kawaja et al., 2006; Thomas et al., 2006). A statistical model helps to regularize the detection, resulting in a more robust delineation. One of the advantages of probabilistic models over traditional methods is that a confidence measure for each segmented signal is given by the log likelihood of the observed signal given the model (Thomas et al., 2006). The main focus of this chapter is to introduce some new and robust HMM associated with wavelet transform based methods for ECG analysis. The chapter begins with a review of the literature on the use of HMM to analyse ECG signals. We then consider in detail the suitability of HMM to provide a faithful statistical description of the ECG. In particular, we examine the validity of the various assumptions inherent in the HMM framework in the context of the ECG. Following this, we consider a number of specific issues in developing an HMM for ECG segmentation, including the choice of model architecture, type of observation models. Then a combination of HMM approach with wavelet properties will be explained.