Abstract—Abstract—In personal healthcare, blood pressure (BP) is an important vital sign to be monitored frequently. However, traditional BP measurement devices require cuff’s inflation and deflation that is very uncomfortable for many users. Cuffless noninvasive BP estimation methods are very attractive especially on using Photoplethysmography (PPG) approach for achieving continuous BP monitoring and minimal user’s inconvenience. From recent studies on the second derivative of PPG (SDPPG) for vascular aging, SDPPG contains the information about aortic compliance and stiffness, which is highly related to blood pressure. To making use of this new finding, 14 new SDPPG based features are proposed in this paper. They are combined with conventional 21 time-scale PPG features to develop a Support Vector Regression based BP estimator. Experimental results demonstrated that the combined features based BP estimator could improve accuracy of the conventional time-scale PPG based BP estimation by 40%.
Index Terms—Index Terms—Blood pressure, photoplethysmography (PPG), second derivative wave, support vector regression.
Mengyang Liu was with City University of Hong Kong, Hong Kong SAR, China. He is now with the Department of Computer Science, Chu Hai College of Higher Education, Hong Kong SAR, China (e-mail: lmyleon2014@gmail.com). Lai-Man Po is with City University of Hong Kong, Hong Kong SAR, China (e-mail: eelmpo@cityu.edu.hk). Hong Fu is with the Computer Science Department, Chu Hai College of Higher Education, Hong Kong SAR, China (e-mail: hongfu@chuhai.edu.hk).
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Cite:Mengyang Liu, Lai-Man Po, and Hong Fu, "Cuffless Blood Pressure Estimation Based on Photoplethysmography Signal and Its Second Derivative," International Journal of Computer Theory and Engineering vol. 9, no. 3, pp. 202-206, 2017.
