Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

Open Access
Research Article
Part of the following topical collections:
  1. Advances in Electrocardiogram Signal Processing and Analysis

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 Model Hide Markov Model Quantum Information Wavelet Transform Wavelet Function 

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Copyright information

© 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.

Authors and Affiliations

  1. 1.Departamento de Engenharia ElétricaUniversidade Federal do Espírito SantoVitóriaBrazil
  2. 2.Département Électronique et PhysiqueInstitut National des TélécommunicationsEvryFrance

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