Abstract—Arrhythmia classification is a very demanding task in medical domain. A great need of handling voluminous ECG data has posed necessity of using artificial intelligence techniques such as artificial neural network (ANN) for detection and classification of these heart beats. In this paper a neural network technique with error back propagation method has been used to classify four different types of arrhythmias, namely, Left bundle branch block (LBBB), Right bundle branch block (RBBB), Atrial premature beat (APB) and Paced Beat (PB) with normal ECG signal. The multilayer perceptron feedforward neural network has been used for modeling the network architecture. The arrhythmic features, on which classification methodology is based, are chosen from morphology of QRS complex.
Index Terms—Artificial neural network, arrhythmia, backpropagation algorithm, confusion matrix, multi layer perceptron.
Indu Saini and B. S. Saini are with Electronics and Communication department at Dr B R Ambedkar National Institute of Technology, Jalandhar, India (e-mail: indu.saini1@gmail.com, sainibss@gmail.com).
Cite: Indu Saini and B. S. Saini, "Cardiac Arrhythmia Classification Using Error Back Propagation Method," International Journal of Computer Theory and Engineering vol. 4, no. 3, pp. 462-464, 2012.
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