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  • Research Article
  • Open Access

Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

EURASIP Journal on Advances in Signal Processing20062007:056215

https://doi.org/10.1155/2007/56215

Received: 1 May 2006

Accepted: 20 November 2006

Published: 27 December 2006

Abstract

This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.

Keywords

Markov ModelHide Markov ModelQuantum InformationWavelet TransformWavelet Function

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Authors’ Affiliations

(1)
Departamento de Engenharia Elétrica, Universidade Federal do Espírito Santo, Vitória, Brazil
(2)
Département Électronique et Physique, Institut National des Télécommunications, Evry, France

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Copyright

© R. V. Andre˜ao and J. Boudy. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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