Abstract
In this study, a restricted coulomb energy network trained by genetic algorithms (GARCE) is proposed for ECG (electrocardiogram) waveform detection. After the R peak of the QRS complex is detected, a window containing an ECG period is formed around the R peak. The significant frequency components of the discrete Fourier transform of the signal in this window are used to form the feature vectors. Restricted Coulomb energy (RCE), multilayer perceptron (MLP) and GARCE networks are comparatively examined to detect 7 different ECG waveforms. The comparative performance results of these networks indicate that the GARCE network results in faster learning and better classification performance with less number of nodes.
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