RESEARCH ARTICLE


Cardiac Health Diagnosis Using Higher Order Spectra and Support Vector Machine



Chua Kuang Chua*, 1, Vinod Chandran2, Rajendra U Acharya1, Lim Choo Min1
1 Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
2 Queensland University of Technology, Australia


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© Chua et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; E-mails: aru@np.edu.sg, ckc@np.edu.sg


Abstract

The Electrocardiogram (ECG) is an important bio-signal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. 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. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification.

Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

Keywords: Heart rate, bispectrum, bicoherence, SVM, classifier.