Adaptive neuro-fuzzy inference system for classification of ECG signal

The heart is one of the important parts of any human being. The heart produces electrical signals thus electrical signals are normally called as Electrocardiogram (ECG) signal. The Electrocardiogram signal is used for identifying the heart problems. The objective of this work is to implement an ANFIS algorithm for (ECG) signals classification. In this work, the classification is done using the ANFIS associated with back propagation algorithm. The ANFIS model is combination of adaptive capabilities with neural network the qualitative approach of fuzzy logic. The feature selection process is done before classification. Four types of ECG beats are collected from the PhysioBank databases. These heart signals are classified by four ANFIS classifiers. The fifth ANFIS classifier is used to get an improved diagnostic accuracy in the ECGs.