Estimation system of blood pressure variation with photoplethysmography signals using multiple regression analysis and neural network

6Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.

Abstract

In this study, a target is to improve the accuracy of a blood pressure (BP) estimation system using photoplethysmography (PPG) signals. A BP estimation algorithm using multiple regression analysis is proposed and a BP estimation using the neural network is studied. Experimental results have shown that estimation accuracy can be improved. Estimation error of systolic BP value using multiple regression analysis with the proposed algorithm was reduced by approximately 16.3%. Furthermore, estimation error was reduced by approximately 21.6% than conventional multiple regression analysis in case of a BP estimation by machine learning using the neural network. It has been found that estimation accuracy is improved and shows the possibility of BP estimation using the neural network.

Cite

CITATION STYLE

APA

Cho, S. I., Negishi, T., Tsuchiya, M., Yasuda, M., & Yokoyama, M. (2018). Estimation system of blood pressure variation with photoplethysmography signals using multiple regression analysis and neural network. International Journal of Fuzzy Logic and Intelligent Systems, 18(4), 229–236. https://doi.org/10.5391/IJFIS.2018.18.4.229

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free