ECG beat classification by a novel hybrid neural network

This paper presents a novel hybrid neural network structure for the classification of the electrocardiogram (ECG) beats. Two feature extraction methods: Fourier and wavelet analyses for ECG beat classification are comparatively investigated in eight-dimensional feature space. ECG features are determ...

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Bibliographic Details
Published in:Computer methods and programs in biomedicine 2001-09, Vol.66 (2), p.167-181
Main Authors: Dokur, Zümray, Ölmez, Tamer
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Classification
Computer Simulation
Computerized, statistical medical data processing and models in biomedicine
Databases, Factual
Decision Making, Computer-Assisted
ECG beat classification
Electrocardiography - statistics & numerical data
Electrocardiography. Vectocardiography
Electrodiagnosis. Electric activity recording
General aspects. Methods
Genetic algorithms
Humans
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Neural networks
Neural Networks (Computer)
Signal Processing, Computer-Assisted
Wavelet
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Summary:This paper presents a novel hybrid neural network structure for the classification of the electrocardiogram (ECG) beats. Two feature extraction methods: Fourier and wavelet analyses for ECG beat classification are comparatively investigated in eight-dimensional feature space. ECG features are determined by dynamic programming according to the divergence value. Classification performance, training time and the number of nodes of the multi-layer perceptron (MLP), restricted Coulomb energy (RCE) and a novel hybrid neural network are comparatively presented. In order to increase the classification performance and to decrease the number of nodes, the novel hybrid structure is trained by the genetic algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 96% by using the hybrid structure.
ISSN:0169-2607
1872-7565
DOI:10.1016/S0169-2607(00)00133-4