Classification of RR-Interval and Blood Pressure Signals Using Support Vector Machine for different Postures

Abstract

In this paper, the classification of RR-interval and blood pressure series for two different physical activities postures has been performed using support vector machine (SVM). Without understanding the changes in these features from lying to standing posture in the same subject it is not possible to decipher the hidden dynamics of cardiovascular control. Thus classification of the subjects based on their RR-interval and blood pressure series, prior to spectral analysis, is essential. Therefore support vector machine, a classifier motivated from statistical learning theory, is used here for classifying the subjects based on lying and standing postures. The efficiency of SVM lies in the choice of the kernel for a given problem. Here in this paper a comparative study has been performed between Linear, Polynomial and Radial Basis kernel functions, and based on highest classification accuracy linear kernel function is proposed for SVM classifier for deciphering the postural related changes in RR-interval and blood pressure signals.