Higher order spectra based support vector machine for arrhythmia classification

Chua, K.C., Chandran, V., Acharya, U.R., & Lim, C.M. (2009) Higher order spectra based support vector machine for arrhythmia classification. In Lim, Chwee Teck & Goh, James C.H. (Eds.) Proceedings of the 13th International Conference on Biomedical Engineering, IFMBE. Springer Berlin Heidelberg, pp. 231-234.

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Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

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ID Code: 78757
Item Type: Book Chapter
DOI: 10.1007/978-3-540-92841-6_56
ISBN: 9783540928409
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2009 Springer Berlin Heidelberg
Deposited On: 19 Nov 2014 00:31
Last Modified: 29 Oct 2015 16:03

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