A proposed method combining discrete wavelet transform coefficients and a back-propagation neural network classified normal sinus rhythm and 12 different arrhythmias with an overall accuracy of 96.77%.
Automatic detection and classification of cardiac arrhythmias is important for diagnosis of cardiac abnormalities. We propose a method to accurately classify ECG arrhythmias through a combination of wavelets and artificial neural networks (ANN). The ability of the wavelet transform to decompose signal at various resolutions allows accurate extraction/detection of features from non-stationary signals like ECG. A set of discrete wavelet transform (DWT) coefficients, which contain the maximum information about the arrhythmia, is selected from the wavelet decomposition. These coefficients are fed to the back-propagation neural network which classifies the arrhythmias. The proposed method is capable of distinguishing the normal sinus rhythm and 12 different arrhythmias and is robust against noise. The overall accuracy of classification of the proposed approach is 96.77%.
Prasad et al. (Mon,) conducted a other in Cardiac arrhythmias. Wavelets and artificial neural networks (ANN) was evaluated on Overall accuracy of classification. A proposed method combining discrete wavelet transform coefficients and a back-propagation neural network classified normal sinus rhythm and 12 different arrhythmias with an overall accuracy of 96.77%.
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