Performance Evaluation of Neural Networks and Adaptive Neuro Fuzzy Inference System for Classification of Cardiac Arrhythmia

  • Abstract
  • Keywords
  • References
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  • Abstract

    In diagnosing the heart diseases, the beat classification of Electrocardiogram (ECG) plays a vital role. This work proposes a good design of an expert system for classification of the normal beat (N), left bundle branch block beat (L), premature ventricular contraction (V), paced beat (P) , atrial premature beat (A) and right bundle branch block beat (R) using time domain, S-Transform and discrete wavelet transform (DWT). The extracted feature set is given to adaptive neuro fuzzy inference system (ANFIS) and artificial neural network (ANN) for classification. The performance analyses on various normal ECG and abnormal ECG signals from 44 subjects of the MIT-BIH arrhythmia database. In this work, the performances of three neural networks, Cascade forward back propagation, nonlinear autoregressive exogenous model (NARX) and Elman back propagation are compared with ANFIS. Contrast to the neural networks the ANFIS shows better performance in the experimental outcomes.



  • Keywords

    Electrocardiogram; discrete wavelet transform; S-Transform; ANN; ANFIS

  • References

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Article ID: 28707
DOI: 10.14419/ijet.v7i4.22.28707

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