ANALYSIS OF ECG WAVE USING PERCEPTRON NEURAL NETWORK

Automatic Detection and classification of Cardiac abnormalities and Arrhythmias from a limited number of ECG signals is of considerable importance in critical care or operating room patient monitoring. We propose a method to accurately classify the heartbeat of ECG signals through the Perceptron Neural Networks. Feature sets are based on QRS complex of the ECG signal. The proposed method is capable of distinguishing the normal beat and 6 different arrhythmias. The results of the analysis are found to be more accurate and easily calculated than the other existing methods. Detection and classification of cardiac signals is important for diagnosis of cardiac abnormalities and hence any automated processing of the ECG that assists this process would be of assistance and is the focus of this paper.