基于HHT的人体脉搏特征识别及分类研究  

Research on human pulse feature recognition and classification based on HHT

作　　者：郭成龙 陈海初 王志锋 Guo Chenglong;Chen Haichu;Wang Zhifeng(Foshan University,Foshan 528200,China)

机构地区：[1]佛山科学技术学院,佛山528200

出　　处：《电子测量技术》2021年第19期116-121,共6页Electronic Measurement Technology

基　　金：国家自然科学基金(61973294)项目资助。

摘　　要：为了快速获取人体脉搏信号完整特征信息,并快速准确识别脉搏特征信息与人体疾病关联表征。研究采用多周期脉搏时域特征和基于集合模态经验分解(EEMD)的希伯尔特黄变换(HHT)获取瞬时频率及幅值作为频域特征,把时域及频域特征进行融合作为卷积神经网络的输入进行人体脉搏特征的识别及分类。从MIT-BIH标准数据库中获取到3种临床症状的脉搏信号进行了实验分析,最后经过实验得到脉搏特征识别及分类准确率为91.88%。采用基于EEMD的HHT作为时域特征的补充,时频特征混合能够使得PPG脉搏信号完整的表征,并在卷积神经网络上进行分类实验,得到较好的分类效果。研究方法愿为临床诊断智能化发展、提高临床诊断的准确率及效率提供良好的促进作用。In order to quickly obtain the complete feature information of the human pulse signal,and quickly and accurately identify the correlation between the pulse feature information and the human disease.The study uses the time-domain characteristics of multi-period pulses and the Hilbert-Huang-transform(HHT)based on ensemble empirical mode decomposition(EEMD)to obtain the instantaneous frequency and amplitude as the frequency-domain characteristics.The time domain and frequency domain features as input fused convolutional neural network to identify and classify the pulse characteristics of the human body.The pulse signals of three clinical symptoms were obtained from the MIT-BIH standard database for experimental analysis.Finally,through experiments,the accuracy of pulse feature recognition and classification is 91.88%.Using EEMD-based HHT as a supplement to time-domain features,time-frequency feature mixing can make the PPG pulse signal complete characterization,and perform classification experiments on the convolutional neural network to obtain better classification results.Methods willing clinical diagnosis of intelligent development,improve the accuracy and efficiency of clinical diagnosis to provide a good role in promoting.

关 键 词：脉搏特征提取 特征分类识别 希伯尔特黄变换(HHT) 集合经验模态分解(EEMD) 卷积神经网络 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]