Does a combined feature SVR-based BP estimator improve the accuracy of cuffless blood pressure estimation compared to conventional time-scale PPG methods?
Combining second derivative PPG features with conventional time-scale features using Support Vector Regression significantly improves the accuracy of cuffless blood pressure estimation.
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-Blood pressure, photoplethysmography (
Liu et al. (2017) studied this question.