Classification of Atrial fibrillation ECG and Malignant Ventricular Arrhythmia ECG using Adaptive Neuro-Fuzzy Interface System

-Now a day we have various types of intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proving to be skillful when applied to a different kind of problems. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. here we applied tool for detecting the two different type of abnormal ECG signal. Here the designed ANFIS model contained both approaches the neural network adaptive potential approach and the fuzzy logic qualitative approach.The Electrocardiogram (ECG) dynamic and nonlinear signal characteristic requires an accurate and precise detection and recognition system. This paper describes the detection of a MIT-BHI normal Artial Fibrillation ECG signal and MIT-BHI Malignant Ventricular ECG signal based on ANFIS approach. Some conclusions regarding the detection and classification of the abnormal ECG signals is obtained through analysis of the ANFIS. The proposed ANFIS modal gives the 100% accuracy for Artial Fibrillation ECG detection and 80% accuracy for Malignant Ventricular ECG detection. Classification accuracies and the results created by the ANFIS confirmed that the proposed ANFIS model is very efficient in classifying the normal and abnormal ECG signals.