SPLINE ACTIVATED NEURAL NETWORK FOR CLASSIFYING CARDIAC ARRHYTHMIA
R. Ganesh Kumar and Y. S. Kumaraswamy
DOI : 10.3844/jcssp.2014.1582.1590
Journal of Computer Science
Volume 10, Issue 8
Electro Cardiogram’s (ECG) biomedical signals characterizing cardiac anomalies are used for identifying cardiac arrhythmia. Irregular heartbeat-Arrhythmia-affects heart rate causing problems. Many methods, trying to simplify arrhythmia monitoring through automated detection, were developed over the years. ECG classification for arrhythmia is investigated in this paper based on soft computing techniques. RR interval are extracted from time series of the ECG and used as feature for arrhythmia classification. Frequency domain extracted features are classified using Radial Basis Function (RBF) and proposed Spline Activated-Feed Forward Neural Network (SA-FFNN). Experiments were conducted with the Massachusetts Institute of Technology-Boston’s Beth Israel Hospital (MIT-BIH) arrhythmia database for evaluating the proposed methods.
© 2014 R. Ganesh Kumar and Y. S. Kumaraswamy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.