基于多参数特征融合的无袖带连续血压测量方法  

Cuffless Continuous Blood Pressure Measurement Method Based on Multi-parameter Feature Fusion

作　　者：金星亮 万程 谢晨杰 刘三超 吴丹[2] JIN Xingliang;WAN Cheng;XIE Chenjie;LIU Sanchao;WU Dan(Shenzhen Mindray Bio-Medical Electronics Co.,Ltd.,Shenzhen 518057,China;Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China;Southern University of Science and Technology,Shenzhen 518055,China)

机构地区：[1]深圳迈瑞生物医疗电子股份有限公司,深圳518057 [2]中国科学院深圳先进技术研究院,深圳518055 [3]南方科技大学,深圳518055

出　　处：《集成技术》2023年第2期29-38,共10页Journal of Integration Technology

基　　金：国家自然科学基金青年基金项目(81701788);广东省自然科学基金面上项目(2022A1515011217)。

摘　　要：血压是人体的生理指标,连续测量患者每个心动周期的动脉血压数据,是医护人员对患者实时诊断的重要依据。现有的无袖带连续血压测量方法,大多基于脉搏波和心电图两路信号获取特征并进行预测建模,无法涵盖影响血压的多种因素,模型存在一定误差。该文对55位志愿者进行实验,在传统脉搏波和心电信号的基础上,引入心阻抗图等体征信息,探索影响血压测量精度的因素。实验结果表明,基于多参数特征融合的随机森林模型的性能优于基于单个特征的线性模型,其对于收缩压和舒张压预测的平均绝对误差分别为2.56 mmHg、1.91 mmHg。该实验证明了基于多特征融合的无袖带血压预测模型可提高血压预测的精度。Blood pressure is a physiological indicator of human body.Continuous measurement of arterial blood pressure in each cardiac cycle is an important basis for real time diagnoses.Most of the cuffless continuous blood pressure measurements are performed according to the predictive models based on the pulse wave and electrocardiogram signals.However,they may produce errors due to the limited measurements.In this paper,multiple physical signs,such as impedance cardiogram,are explored to improve the measured accuracy of blood pressure.Experiments were conducted upon 55 volunteers,and results show that the random forest model based on multi-parameter feature fusion outperformed the linear model based on a single feature,with mean absolute errors of 2.56 mmHg and 1.91 mmHg for the prediction of systolic and diastolic blood pressure,respectively.It proves that the proposed cuffless blood pressure prediction model based on the multifeature fusion could improve the accuracy of blood pressure prediction.

关 键 词：连续血压 无袖带 多参数 机器学习 心阻抗图 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]