Abstract

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Title:: Cardiovascular disease recognition based on electrocardiogram data and pulse wave

Author(s):: CHEN Qianrong1; LIANG Yongbo2; ZHAO Feijun2; ZHU Jianming2; CHEN Zhencheng2; 1. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China; 2. School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China

Keywords:: ; Keywords: cardiovascular disease; pulse wave; K-nearest neighbor; support vector machine

Abstract:: Abstract: To achieve an early screening of cardiovascular diseases and reduce the cost of clinical detection of cardiovascular disease, a research based on pulse wave velocity (PWV) in the upper extremities and the hemodynamic theory related to pulse waves is performed. The pulse waves and electrocardiogram data of 51 volunteers were collected, and 16 feature parameters including 3 types of PWV and pulse wave features were extracted. Three sample feature data sets which are composed of different PWV and pulse wave features are used to establish two different cardiovascular diseases recognition models based on K-nearest neighbor (KNN) or support vector machine (SVM). The clssification accuracy of KNN and SVM models is 66.28% and 84.3%, respectively. By comparing the effects of different PWV on the performance of models, the optimal pwvm for vascular assessment is determined. The research results show that the SVM model is reliable in the cardiovascular disease recognition, providing a new idea for the low-cost and early screening of cardiovascular diseases and providing a basis for wearable cardiovascular system monitoring.