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Article World Academy of Science, Engineering and Technology ; Volume 70 , 2010 , Pages 788-794 ; 2010376X (ISSN) ; Zakernejad, S ; Faridi, S ; Javadi, M ; Ebrahimpour, R ; Sharif University of Technology
This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then...