A Multi-HMM Approach to ECG Segmentation

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A Multi-HMM Approach to ECG Segmentation
Pharmaceutic studies require to analyze thousands of ECGs in order to evaluate the side effects of a new drug. In this paper we present a new approach to automatic ECG segmentation based on hierarchic continuous density hidden Markov models. We applied a wavelet transform to the signals in order to highlight the discontinuities in the modeled ECGs. A training base of standard 12-lead ECGs segmented by cardiologists was used to evaluate the performance of our method. We used a Bayesian HMM clustering algorithm to partition the training base, and we improved the method by using a multi-model approach. We present a statistical analysis of the results where we compare different automatic methods to the segmentation of the cardiologist.
Julien Thomas, Cédric Rose, François
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Authors Julien Thomas, Cédric Rose, François Charpillet
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