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Modelling temporal evolution of cardiac electrophysiological features using Hidden Semi-Markov Models.

Abstract : This paper presents a new method to analyse cardiac electrophysiological dynamics. It aims to classify or to cluster (i.e. to find natural groups) patients according to the dynamics of features extracted from their ECG. In this work, the dynamics of the features are modelled with Continuous Density Hidden Semi-Markovian Models (CDHSMM) which are interesting for the characterization of continuous multivariate time series without a priori information. These models can be easily used for classification and clustering. In this last case, a specific method, based on a fuzzy Expectation Maximisation (EM) algorithm, is proposed. Both tasks are applied to the analysis of ischemic episodes with encouraging results and a classification accuracy of 71%.
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Submitted on : Thursday, October 30, 2008 - 11:06:39 AM
Last modification on : Wednesday, May 16, 2018 - 11:23:08 AM
Long-term archiving on: : Monday, June 7, 2010 - 8:48:53 PM

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Jérôme Dumont, Alfredo Hernandez, Julien Fleureau, Guy Carrault. Modelling temporal evolution of cardiac electrophysiological features using Hidden Semi-Markov Models.. Conference proceedings : .. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Institute of Electrical and Electronics Engineers (IEEE), 2008, 2008, pp.165-8. ⟨10.1109/IEMBS.2008.4649116⟩. ⟨inserm-00335659⟩

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