Adaptive Neuro-Fuzzy Inference System for classification of ECG signals

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%.

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