doi: 10.7763/IJCTE.2012.V4.508
Cardiac Arrhythmia Classification Using Error Back Propagation Method
Indu Saini
,
B. S. Saini
- Electronics and Communication department at Dr B R Ambedkar National Institute of Technology, Jalandhar, India.
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.
Keywords
- Artificial neural network
- arrhythmia
- backpropagation algorithm
- confusion matrix
- multi layer perceptron
How to 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. https://doi.org/10.7763/IJCTE.2012.V4.508
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).