| 陈剑虹,郭亚亚,郑铱,林志强,孙超越.基于光电容积脉搏波特征参数的血管弹性检测[J].电子测量与仪器学报,2021,35(3):11-17 |
| 基于光电容积脉搏波特征参数的血管弹性检测 |
| Blood vessel wall elasticity detection based on characteristic parameters of photoplethysmography |
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| DOI： |
| 中文关键词: 光电容积脉搏波 血管壁弹性 肱踝脉搏波传导速度 支持向量机回归 |
| 英文关键词:photoplethysmography(PPG) vascular wall elasticity baPWV support vector machine regression |
| 基金项目:国家重点研发计划重大科学仪器设备开发重点专项(2017YFF0104403)、陕西省自然科学基金(2018JM6022)项目资助 |
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| Author | Institution |
| Chen Jianhong | School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China |
| Guo Yaya | School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China |
| Zheng Yi | School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China |
| Lin Zhiqiang | School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China |
| Sun Chaoyue | School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China |
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| 中文摘要: |
| 肱踝脉搏波传导速度(baPWV)通常作为血管壁弹性的评价指标，其测量方法是测量四肢肱踝多导信号。针对检测仪器昂贵、操作复杂等问题，设计了一种通过指端采集的光电容积脉搏波进行动脉硬化程度检测的新方法，采用独立脉压指数P1、P2结合与血管壁弹性具有相关性的特征参数K值进行聚类分析，通过改进K_means算法在若干波形周期中选取出代表波形，提取特征参数进行支持向量机回归建模。结果为该方法检测值与医院实测结果的平均相对误差为421%，结果表明该方法是一种简单有效的血管壁弹性检测方法，完全满足实际临床应用需求。 |
| 英文摘要: |
| The brachial ankle pulse wave velocity (baPWV) is usually used as an evaluation index of the elasticity of the blood vessel wall, the commonly used measurement method of baPWV is the method of extremity brachial and ankle poly conduction signals. Aiming at the problem of expensive detection instrument and complex operation, a new method is proposed to detect the degree of arteriosclerosis by collecting the photoelectric volume pulse wave at the fingertip. Clustering analysis is carried out by using independent pulse pressure index P1 and P2 combined with the characteristic parameter K value, which has correlation with the elasticity of the vessel wall. By improving K_means algorithm to select representative waveforms in several waveform cycles, the characteristic parameters are extracted for regression modeling by support vector machine. The results show that the average absolute relative deviation of the detection value is 421%. This results indicating that this method is a simple and effective method for the detection of vessel wall elasticity, which fully meets the actual clinical application requirements. |
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