Journal of Medical Systems

, 38:98 | Cite as

A Wavelet Transform Based Feature Extraction and Classification of Cardiac Disorder

  • S. Sumathi
  • H Lilly Beaulah
  • R. Vanithamani
Patient Facing Systems
Part of the following topical collections:
  1. Patient Facing Systems


This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved.


ECG Symlet wavelet transform Five cardiac arrhythmias Myocardial ischemia ANFIS 


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Mahendra Engineering CollegeSalemIndia

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