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

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Résumé

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.

langue originaleAnglais
titreESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Pages141-146
Nombre de pages6
Etat de la publicationPublié - 2009
Modification externeOui
Evénement17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009 - Bruges, Belgique
Durée: 22 avr. 200924 avr. 2009

Une conférence

Une conférence17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2009
PaysBelgique
La villeBruges
période22/04/0924/04/09

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  • Contient cette citation

    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). Dans ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning (p. 141-146)