Machine learning Algorithm for Non-invasive Blood Pressure Estimation Using PPG Signals

被引：5

作者：

Zhang, Gengjia ^{[1
]}

Shin, Siho ^{[1
]}

Jung, Jaehyo ^{[1
]}

Li, Meina ^{[2
]}

Kim, Youn Tae ^{[1
]}

机构：

[1] Chosun Univ, Dept IT Fus Technol, AI Healthcare Res Ctr, 309 Pilmun Daero, Gwangju 61452, South Korea

[2] Jilin Univ, Coll Instrumentat & Elect Engn, Changchun 130061, Peoples R China

来源：

2022 IEEE FIFTH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND KNOWLEDGE ENGINEERING, AIKE | 2022年

基金：

新加坡国家研究基金会; 中国国家自然科学基金;

关键词：

Blood Pressure; Photoplethysmography; Gradient Boosting Regressor; Cuffless; Machine learning;

D O I：

10.1109/AIKE55402.2022.00022

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this study, we propose a blood pressure estimation algorithm that employs a gradient boosting regressor. A Photoplethysmography obtained from the MIMIC II database is uniformly divided to accurately estimate blood pressure. Blood pressure is estimated by extracting the features from these data. The performance of the algorithm is evaluated by analyzing R-2, MSE, MAE, and time. The MSE of SBP is 7.07 mmHg, MAE is 4.33 mmHg, and R-2 is 0.58. In addition, the MSE of the DBP is 4.18 mmHg, MAE is 2.54 mmHg, and the R-2 is 0.87. This study confirmed the possibility of developing an algorithm that can accurately estimate blood pressure.

引用

页码：94 / 97

页数：4

共 16 条

[1] From Wearable Sensors to Smart Implants-Toward Pervasive and Personalized Healthcare [J].