文献类型：期刊文献

英文题名：A non-invasive blood pressure prediction method based on pulse wave feature fusion

作者：Yan, Jianjun[1];Cai, Xianglei[1];Zhu, Guangyao[1];Guo, Rui[2];Yan, Haixia[2];Wang, Yiqin[2]

机构：[1]East China Univ Sci & Technol, Inst Intelligent Percept & Diag, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]Shanghai Univ Tradit Chinese Med, Shanghai Key Lab Hlth Identificat & Assessment, Lab Tradit Chinese Med Diagnost Informat 4, Shanghai 201203, Peoples R China

年份：2022

卷号：74

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20220511581150);WOS:【SCI-EXPANDED(收录号:WOS:000782646000006)】；

语种：英文

外文关键词：Pulse wave; Feature fusion; Gradient Boosting Decision Tree; Blood pressure prediction

摘要：ABSTR A C T To study the non-invasive blood pressure prediction based on pulse wave feature fusion to achieve rapid blood pressure (BP) measurement and improve the measurement accuracy, which provides a new method for the non-invasive blood pressure measurement by wearable devices. From the pulse signals, 82 dimensional features were extracted, including time domain features extracted by the feature point method, ratio features of pulse wave amplitude and pulse-taking pressure fusion, and pulse wave velocity (PWV) features. Feature fusion is performed by feature importance analysis to reduce the dimensionality, and the fused features are used to build blood prediction models based on gradient boosting decision tree (GBDT) regression algorithm. The correlation co-efficients between the predicted and actual values of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 0.93 and 0.92, respectively, which had high correlation, and the mean absolute errors between the predicted and actual values of SBP and DBP were 3.75 mmHg and 3.10 mmHg, respectively, with standard deviations of 5.46 mmHg and 3.93 mmHg, all of which met the overall performance requirements of the asso-ciation for the advancement of medical instrumentation (AAMI) and British hypertension society (BHS) Inter-national electronic blood pressure monitor. This blood pressure prediction model can be better used in the non-invasive blood pressure measurement of wearable devices, which is more convenient and has higher prediction accuracy compared with mercury sphygmomanometer.