Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

  • Authors:

  • Tom Froese;Sillas Hadjiloucas;Roberto K. H. Galvão;Victor M. Becerra;Clarimar José Coelho

  • Affiliations:

  • Department of Informatics, The University of Sussex, Brighton BN1 9QH, UK;Department of Cybernetics, The University of Reading, P.O. Box 225, Whiteknights Campus, Reading, Berkshire RG6 6AY, UK;Divisão de Engenharia Eletrônica, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP 12228-900, Brazil;Department of Cybernetics, The University of Reading, P.O. Box 225, Whiteknights Campus, Reading, Berkshire RG6 6AY, UK;Departamento de Ciência da Computação, Universidade Católica de Goiás, Goiínia, GO 74605-010, Brazil

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2006

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Abstract

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MIT-BIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data was presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network.