Feature Extraction Based on Pan Tompkins Algorithm from ECG Signals and Diagnosis of Arrhythmia Using Multilayer Perceptron Neural Network

In this study, Electrocardiogram (ECG) signals giving information about the state and functioning of the heart are divided into segments, waves and intervals by resting upon temporal limitations and feature vector of each section is obtained by means of arithmetic mean which is one of basic statistical parameters. Arrhythmia (rhythm disorders) occurring in the heart are diagnosed by the obtained feature vectors used as the inputs into multilayer perceptron neural network (MLPNN) model. For this purpose, ECG signals are divided into sections that are 10-minute-equal-length. These sections are divided into subsections (segment and intervals) which are admitted for each segment and wave interval and give information on arrhythmia by temporal limitations and arithmetic average of each interval is used as the inputs into the model of MLPNN for the diagnosis of arrhythmia. As a conclusion, it is proved that the proposed approach has reached to high accuracy rates of classification for the diagnosis of arrhythmia through ECG signals.