Detection of Normal ECG and Arrhythmia Using Adaptive Neuro-Fuzzy Interface System

Now a day we have various intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proving to be dexterous when applied to a range of problems. In this paper we applied the ANFIS (Adaptive Neuro-Fuzzy Interface System) tool for detecting the normal and abnormal signal. Here the designed ANFIS model contained both approaches the neural network adaptive potential approach and the fuzzy logic qualitative approach. Hybrid method is used as an optimization method. 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 sinus ECG database signal and MIT-BHI Supraventricular ECG database signal based on ANFIS approach. Some conclusions regarding the classification of the ECG signals is obtained through analysis of the ANFIS. The proposed ANFIS modal gives the 100% accuracy for normal ECG detection and 91% accuracy for abnormal 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. but in our research we have taken the 1 min complete ECG include of many ECG bits is taken for analysis.