Regression tree model for blood pressure estimation using the minimalist characteristics of photoplethysmography signal
10.3969/j.issn.1005-202X.2024.06.016
- VernacularTitle:基于PPG信号的极简特征回归树血压估计模型设计
- Author:
Xun LI
1
;
Lirong LIU
;
Hao LI
;
Lianlin YANG
;
Zhimin WANG
;
Mei ZOU
Author Information
1. 昆明学院物理科学与技术学院,云南昆明 650214
- Keywords:
photoplethysmography;
minimalist characteristics;
Spearman correlation coefficient;
blood pressure estimation model
- From:
Chinese Journal of Medical Physics
2024;41(6):769-775
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a regression tree model for the estimation of blood pressure using the minimalist characteristics of photoplethysmography(PPG)signals.Methods Fifteen characteristic parameters were extracted from the PPG signals,and the 4 parameters with the highest correlations with blood pressure were screened using the Spearman correlation coefficient to construct a regression tree model for blood pressure estimation using the minimalist characteristics.Results The estimation errors of systolic and diastolic blood pressures in the constructed model were(-0.02±3.63)mmHg and(-0.04±2.10)mmHg,respectively.Conclusion The proposed regression tree model has a simple structure and high accuracy,which is of great significance for using a single-channel PPG signal for blood pressure estimation in wearable devices.
