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Adaptive Neuro-Fuzzy Inference System for classification of ECG signals

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3 Author(s)
T. M. Nazmy ; Faculty of computer and information sciences, Ain Shams University, Cairo, Egypt ; H. El-Messiry ; B. Al-Bokhity

This paper, presents an Intelligent diagnosis system using Hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. Feature extraction using Independent Component Analysis (ICA) and Power spectrum, together with the RR interval then serve as input feature vector, this feature were used as input of ANFIS classifiers. six types of ECG signals they are normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC), Ventricular Tachycardia(VT), Ventricular Fibrillation (VF) and Supraventricular Tachycardia (SVT). The proposed ANFIS model combined the Neural Network adaptive capabilities and the fuzzy Inference System. The results indicate a high level of efficient of tools used with an accuracy level of more than 97%.

Published in:

Informatics and Systems (INFOS), 2010 The 7th International Conference on

Date of Conference:

28-30 March 2010