Markov Models for Automated ECG Interval Analysis
Abstract
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.
Cite
Text
Hughes et al. "Markov Models for Automated ECG Interval Analysis." Neural Information Processing Systems, 2003.Markdown
[Hughes et al. "Markov Models for Automated ECG Interval Analysis." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/hughes2003neurips-markov/)BibTeX
@inproceedings{hughes2003neurips-markov,
title = {{Markov Models for Automated ECG Interval Analysis}},
author = {Hughes, Nicholas P. and Tarassenko, Lionel and Roberts, Stephen J.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {611-618},
url = {https://mlanthology.org/neurips/2003/hughes2003neurips-markov/}
}