Markov Models for Automated ECG Interval Analysis

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

Bibtex Metadata Paper


Nicholas Hughes, Lionel Tarassenko, Stephen J. Roberts


We examine the use of hidden Markov and hidden semi-Markov mod- els for automatically segmenting an electrocardiogram waveform into its constituent waveform features. An undecimated wavelet transform is used to generate an overcomplete representation of the signal that is more appropriate for subsequent modelling. We show that the state dura- tions implicit in a standard hidden Markov model are ill-suited to those of real ECG features, and we investigate the use of hidden semi-Markov models for improved state duration modelling.