Title: ECG classification using morphological features derived from symbolic dynamics

Authors: Chandrakar Kamath

Addresses: Electronics and Communication Department, Manipal Institute of Technology, Manipal 576104, India

Abstract: The aim of this study is to estimate how far the symbolic analysis helps to characterize the nonlinear properties of Electrocardiogram (ECG) signals and thereby discriminate between normal and arrhythmia signals. Differences were found in the properties of the symbol sequence histogram of the resulting patterns and complexity measures of symbol sequences among the five different classes, namely normal, left bundle branch block (LBBB), right bundle branch block (RBBB), premature-ventricular contraction (PVC) and paced signals. The efficacy of the symbolic analysis features is also shown through classification achieved through the neural network which exhibits an average accuracy that exceeds 93.5%.

Keywords: ECG signals; electrocardiograms; normal signals; arrhythmia signals; symbolic dynamics; neural networks; ECG classification; morphological features.

DOI: 10.1504/IJBET.2012.049217

International Journal of Biomedical Engineering and Technology, 2012 Vol.9 No.4, pp.325 - 336

Published online: 12 Dec 2014 *

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