The week's pick
Reseach Article
Support Vector Machine for Abnormal Pulse Classification
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 22 - Number 7 |
| Year of Publication: 2011 |
| Authors: Bhaskar Thakker, Anoop Lal Vyas |
10.5120/2597-3610
|
Bhaskar Thakker, Anoop Lal Vyas . Support Vector Machine for Abnormal Pulse Classification. International Journal of Computer Applications. 22, 7 ( May 2011), 13-19. DOI=10.5120/2597-3610
Abstract
Radial pulse signals have been utilized in ancient culture for the health diagnosis due to its simple, non invasive and effective approach. Characteristics of a newly identified abnormal pulse in the subjects suffering from gastritis and arthritis are discussed along with commonly visible healthy pulse patterns in this work. A binary classifier to segregate such abnormal pulses from healthy pulse patterns is modeled using linear, quadratic as well as support vector machine based algorithms. Frequency domain features derived from power spectral density of the pulse signal are ranked to achieve dimensionality reduction. It has been found that the support vector machine with linear kernel classifies the abnormal pulse signals with highest success rate of 99.2% utilizing only two ranked frequency domain features.
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