A Continuous Non-invasive Blood Pressure Prediction Method Based on Improved SVR Learning

Authors

Haixia Fan, 1. College of Electrical Engineering, Nanjing institute of industry technology, Nanjing 210023, China; ;
Xiaohui Chen, 2. College of Automation/Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;

Abstract

Abstract: Aiming at the accuracy of continuous non-invasmive monitoring of blood pressure by photoelectric method based on the photoplethysmography (PPG) signal and the electrocardiography (ECG) signal, is influenced by the differences of human characteristics, a blood pressure prediction method based on principal component analysis (PCA) and genetic algorithm (GA) to optimize machine learning model is proposed. The method processes the PPG signal, ECG signal and human body features to form a feature matrix, and uses an improved SVR learning model to perform regression training on the feature matrix and the real-time blood pressure value measured by the mercury sphygmomanometer. The GA is used to optimize the parameters to establish an optimal blood pressure prediction model. The experimental results show that, compared with the traditional SVR, the proposed method could improve the predictive accuracy by 10%-15%.

Recommended Citation

Fan, Haixia and Chen, Xiaohui (2020) "A Continuous Non-invasive Blood Pressure Prediction Method Based on Improved SVR Learning," Journal of System Simulation: Vol. 32: Iss. 9, Article 6.
DOI: 10.16182/j.issn1004731x.joss.19-0154
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss9/6

First Page

1686

Revised Date

2020-03-21

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-0154

Last Page

1692

CLC

TP274

Recommended Citation

Fan Haixia, Chen Xiaohui. A Continuous Non-invasive Blood Pressure Prediction Method Based on Improved SVR Learning[J]. Journal of System Simulation, 2020, 32(9): 1686-1692.

DOI

10.16182/j.issn1004731x.joss.19-0154