A non-invasive blood pressure prediction method based on pulse wave feature fusion
Jianjun Yan, Xianglei Cai, Guangyao Zhu, Rui Guo, Haixia Yan, Yiqin Wang
Abstract
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 coefficients 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 association for the advancement of medical instrumentation (AAMI) and British hypertension society (BHS) International 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.