基于脉象分析的亚健康状态识别  

Sub-health identification based on pulse analysis

作　　者：莫太平[1] 王彦丽 MO Taiping;WANG Yanli(School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China)

机构地区：[1]桂林电子科技大学电子工程与自动化学院,广西桂林541004

出　　处：《桂林电子科技大学学报》2017年第6期442-446,共5页Journal of Guilin University of Electronic Technology

基　　金：国家自然科学基金(60964001);广西自然科学基金(D200220)

摘　　要：为了提高国民健康水平,快速检测亚健康状态,通过研究人体脉象的变化,设计了用于亚健康识别的脉象检测系统。该系统分为信号采集模块和分类识别模块。信号采集模块使用压力传感器采集人体脉搏信号,对信号中存在的噪声干扰,运用8层小波分析对脉搏信号进行滤波去噪,并对去噪后的波形进行周期化分割和重采样,提高了提取的特征值精度。分类识别模块根据特征值构建特征向量,并通过支持向量机建立亚健康分类模型,对健康人群和亚健康人群的脉搏信号进行分类识别。实验结果表明,该系统对亚健康状态的识别达到了较高的准确率。In order to improve the national health level and body pulse are studied, the pulse detection system is design rapidly ed. The detect the sub-health status, the changes in the human system is divided into signal acquisition module and clas- sification recognition module. The signal acquisition module uses the pressure sensor noise interference in the signal, the pulse signal is denoised by 8 layer wavelet analysis to collect and the signal. denoised waveform is seg- mented and resampled. The accuracy of the extracted eigenvalues is improved. The classification recognition module establi- shes the eigenveetor according to the eigenvalue and the sub health recognition model through the support vector machine. The experimental results show that the system has higher accuracy in the recognition of sub-health status.

关 键 词：亚健康 脉诊 小波分析 支持向量机 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]