Key result
Restricted Coulomb energy neural network classifies multiple ECG beat types with a 97% success rate.
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Supports ECG algorithm development; leaves open prospective clinical validation before any practice integration.
Two feature extraction methods, Fourier and wavelet analyses for ECG beat classification, are comparatively investigated. ECG features are searched by dynamic programming according to the divergence values. 10 types of ECG beat from an MIT-BIH database are classified with a success of 97% using a restricted Coulomb energy neural network trained by genetic algorithms.
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Dokur et al. (1999) studied this question. A restricted Coulomb energy neural network trained by genetic algorithms using Fourier and wavelet feature extraction classified 10 types of ECG beats with a 97% success rate.
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