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Estimation System of Blood Pressure Variation with Photoplethysmography Signals Using Multiple Regression Analysis and Neural Network
Int. J. Fuzzy Log. Intell. Syst. 2018;18(4):229-236
Published online December 25, 2018
© 2018 Korean Institute of Intelligent Systems.

Seung-Il Cho1;2, Takumi Negishi2, Minami Tsuchiya2, Muneki Yasuda2 and Michio Yokoyama2

1Innovation Center for Organic Electronics, Yamagata University, Yamagata, Japan 2Graduate School of Science and Engineering, Yamagata University, Yamagata, Japan
Correspondence to: Michio Yokoyama (
Received December 1, 2018; Revised December 13, 2018; Accepted December 21, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords : Blood pressure estimation, Multiple regression analysis, Neural network, Correlation coefficient, Photoplethysmography