Improving the transition modelling in hidden Markov models for ECG segmentation: Proceedings of the 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN 2009)

Benoît Frénay, Gaël de Lannoy, Michel Verleysen

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Abstract

The segmentation of ECG signal is a useful tool for the diagnosis of cardiac diseases. However, the state-of-the-art methods use hidden Markov models which do not adequately model the transitions between successive waves. This paper uses two methods which attempt to overcome this limitation: a HMM state scission scheme which prevents ingoing and outgoing transitions in the middle of the waves and a bayesian network where the transitions are emission-dependent. Experiments show that both methods improve the results on pathological ECG signals.

Original languageEnglish
Title of host publicationESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Pages141-146
Number of pages6
Publication statusPublished - 2009
Externally publishedYes
Event17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009 - Bruges, Belgium
Duration: 22 Apr 200924 Apr 2009

Conference

Conference17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009
CountryBelgium
CityBruges
Period22/04/0924/04/09

Keywords

  • ECG segmentation Transition modelling Hidden Markov models ICTEAM:MLAI

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    Frénay, B., de Lannoy, G., & Verleysen, M. (2009). Improving the transition modelling in hidden Markov models for ECG segmentation: Proceedings of the 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (ESANN 2009). In ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (pp. 141-146)