Research Article Open Access

SPLINE ACTIVATED NEURAL NETWORK FOR CLASSIFYING CARDIAC ARRHYTHMIA

Y. S. Kumaraswamy1 and R. Ganesh Kumar2
  • 1 , India
  • 2 Sathyabama University, India

Abstract

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.

Journal of Computer Science
Volume 10 No. 8, 2014, 1582-1590

DOI: https://doi.org/10.3844/jcssp.2014.1582.1590

Submitted On: 19 December 2013 Published On: 9 April 2014

How to Cite: Kumaraswamy, Y. S. & Kumar, R. G. (2014). SPLINE ACTIVATED NEURAL NETWORK FOR CLASSIFYING CARDIAC ARRHYTHMIA. Journal of Computer Science, 10(8), 1582-1590. https://doi.org/10.3844/jcssp.2014.1582.1590

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Keywords

  • Multilayer Perceptron
  • Feed Forward Neural Network
  • RR Interval
  • Arrhythmia Classification
  • ECG