A trainable neural network ensemble for ECG beat classification

Sajedin, A ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. Publisher: 2010
  3. Abstract:
  4. 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 feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptrons (MLPs) neural networks with different topologies are designed. The performance of the different combination methods as well as the efficiency of the whole system is presented. Among them, Stacked Generalization as a proposed trainable combined neural network model possesses the highest recognition rate of around 95%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems. ECG samples attributing to the different ECG beat types were extracted from the MIT-BIH arrhythmia database for the study
  5. Keywords:
  6. ECG beat Classification ; Premature Ventricular Contraction (PVC) ; Beat classification ; Combination method ; Combined neural networks ; Combining classifiers ; De-noising ; Diagnosis systems ; ECG signals ; Electrocardiogram signal ; Heart disease ; Morphological features ; Multi Layer Perceptrons ; Neural network ensembles ; Noise reductions ; Premature ventricular contraction ; Recognition rates ; Stacked generalization ; Stationary wavelet transforms ; Three stages ; Timing interval ; Whole systems ; Multi layer perceptrons ; Audio systems ; Classifiers ; Cybernetics ; Electrochromic devices ; Feature extraction ; Health care ; Multilayer neural networks ; Pattern recognition systems ; Wavelet transforms ; Wireless sensor networks ; Electrocardiography
  7. Source: World Academy of Science, Engineering and Technology ; Volume 70 , 2010 , Pages 788-794 ; 2010376X (ISSN)
  8. URL: http://waset.org/publications/915/a-trainable-neural-network-ensemble-for-ecg-beat-classification