Support Vector Machine for Abnormal Pulse Classification

Abstract

See, stats, and : https : / / www . researchgate . net / publication / 252668206 Support Classification Article DOI : 10 . 5120 / 2597 - 3610 CITATIONS 3 READS 23 2 , including : Bhaskar G 11 SEE All - text , letting . Available : Bhaskar Retrieved : 18 ABSTRACT Radial pulse signals have been utilized in ancient culture health diagnosis due to its simple , non invasive approach . Characteristics of a newly identified abnormal pulse in the subjects suffering from gastritis and arthritis are along with commonly visible healthy pulse patterns in this work A binary classifier to segregate such abnormal pulse 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 .