Cardiac disease classification using heart rate signals Online publication date: Fri, 16-Jul-2010
by V. Mahesh, A. Kandaswamy, C. Vimal, B. Sathish
International Journal of Electronic Healthcare (IJEH), Vol. 5, No. 3, 2010
Abstract: Heart rate and Heart Rate Variability (HRV) are important measures that reflect the state of the cardiovascular system. HRV analysis has gained prominence in the field of cardiology for detecting cardiac abnormalities. This paper presents the study made on the use of linear (time domain and frequency domain) and nonlinear measures of heart rate variability for accurate classification of certain cardiac diseases. Three different classifiers, viz. Random Forests, Logistic Model Tree and Multilayer Perceptron Neural Network have been used for the classification. Data for use in this work has been obtained from the standard ECG databases in the Physionet website. Classification has been attempted using linear parameters, nonlinear parameters and combined. The classification results indicate that the combination of linear and nonlinear measures is a better indicator of heart diseases than linear or nonlinear measures alone. The results obtained by this study are comparable with those obtained with other techniques cited in the literature.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Electronic Healthcare (IJEH):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook