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

摘要：

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 RRinterval and blood pressure signals.

关键词： Classifier Support Vector Machine Kernel functions Postures

作者: Indu Saini Arun Khosla Dilbag Singh

作者单位: B R Ambedkar National Institute of Technology Jalandhar, Jalandhar- 144011, India

会议类型: 国际会议

会议名称: 2011 International Conference on Database and Data Mining(ICDDM 2011)(2011年数据库和数据挖掘国际会议)

会议地点: 三亚

会议语种:英文

页码: 138-142

在线出版日期: 2011-03-25（万方平台首次上网日期，不代表论文的发表时间）