Statistical models based ECG classification - Archive ouverte HAL
Chapitre D'ouvrage Année : 2009

Statistical models based ECG classification

Résumé

This chapter gives a comprehensible description of two statistical approaches successfully employed to the problem of beat modeling and classification: hidden Markov models and hidden Markov trees . The HMM is a stochastic state machine which models a beat sequence as a cyclostationary Markovian process. It offers the advantage of performing both beat modeling and classification through a unique statistical approach. The HMT exploits the persistence property of the wavelet transform by associating to each wavelet coefficient a state and the states are connected across scales to form a probabilistic graph. This method can also be used for signal segmentation
Ce chapitre traite des aspects de reconnaissance des formes appliquées à la classification d'ECG pathologiques (arythmies, ischémies cardiaques). Deux approches sont proposées, développées et evaluées: un modèle de sous-battements basé sur les HMM et une approche d'Arbre de Markov Cachés (HMT).

Dates et versions

hal-01316467 , version 1 (17-05-2016)

Identifiants

Citer

Rodrigo Andreao, Jérôme Boudy, Bernadette Dorizzi, Jean-Marc Boucher, Salim Graja. Statistical models based ECG classification. Advanced biosignal processing, Springer-Verlag, pp.71 - 93, 2009, 978-3-540-89505-3. ⟨10.1007/978-3-540-89506-0_4⟩. ⟨hal-01316467⟩
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