ENSEMBLE OF NEURAL NETWORKS FOR IMPROVED RECOGNITION AND CLASSIFICATION OF ARRHYTHMIA

S. Osowski, T. Markiewicz, L. Tran Hoai

Abstract

The paper presents different methods of combining many neural classifiers into one ensemble system for recognition and classification of arrhythmia. Majority and weighted voting, Kullback-Leibler divergence and modified Bayes methods will be presented and compared. The numerical experiments will be performed for the problems concerning the recognition of different types of arrhythmia on the basis of ECG waveforms of MIT BIH AD.

Keywords

neural classifiers, ensemble of classifiers, methods of integrations, arrhythmia recognition

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PWC-2006-TC13-005u.pdf

DOI

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IMEKO TC

TC13 - Measurements in Biology and Medicine

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