학술논문

Estimation System of Blood Pressure Variation with Photoplethysmography Signals Using Multiple Regression Analysis and Neural Network
Document Type
Academic Journal
Source
INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS. 2018-12 18(4):229-236
Subject
Blood pressure estimation
Multiple regression analysis
Neural network
Correlation coefficient
Photoplethysmography
Language
Korean
ISSN
1598-2645
2093-744X
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.