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Comparison of discrete wavelet and Fourier transforms for ECG beat classification

Comparison of discrete wavelet and Fourier transforms for ECG beat classification

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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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