A Multi-HMM Approach to ECG Segmentation

Julien Thomas 1 Cédric Rose 1 François Charpillet 1
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : 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.
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18th IEEE International Conference on Tools with Artificial Intelligence - ICTAI'06, Nov 2006, Washington D.C., United States. IEEE, pp.609-616, 2006, 18th IEEE International Conference on Tools with Artificial Intelligence, 2006. ICTAI '06. 〈10.1109/ICTAI.2006.17〉
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Julien Thomas, Cédric Rose, François Charpillet. A Multi-HMM Approach to ECG Segmentation. 18th IEEE International Conference on Tools with Artificial Intelligence - ICTAI'06, Nov 2006, Washington D.C., United States. IEEE, pp.609-616, 2006, 18th IEEE International Conference on Tools with Artificial Intelligence, 2006. ICTAI '06. 〈10.1109/ICTAI.2006.17〉. 〈inria-00095437〉

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