基于局部均值分解的脉搏信号变异性分析  

Pulse signal variability analysis based on local mean decomposition

作　　者：康素成[1] Kang Sucheng(Yancheng Teachers University,Yancheng 224007,China)

机构地区：[1]盐城师范学院,江苏盐城224007

出　　处：《无线互联科技》2020年第22期107-109,共3页Wireless Internet Technology

摘　　要：文章介绍脉搏PRV信号变异性与局部均值分解(LMD),提出时域提取PRV信号,运用熵分析方法,设计实验,选择包括20名年轻人与20名年老的一般健康人员与不抽烟的健康人员,运用LabVIEW软件进行数据采集,对样品脉搏数据进行实时采样、预处理,进行诊断分析,频域提取PIF信号。结果显示可表征PRV信号,对研究样品年龄、睡眠与疲劳状态下的脉搏信号进行脉率变异符号序列熵分析。基于局部均值分解的脉搏信号变异性分析误差数值较小,可估算出运动状态下脉搏PRV信号,可促进临床PRV信号检测。Objective:to introduce the variability and local mean decomposition(LMD)of pulse PRV signal,and propose time-domain extraction of PRV signal.Methods:the experiment was designed by using entropy analysis method,including 20 young people and 20 elderly general health personnel and non-smoking health personnel Software for data acquisition,real-time sampling and preprocessing of sample pulse data,diagnosis and analysis,extraction of PIF signal in frequency domain.Results:the PRV signal can be represented,and the pulse signal under the condition of age,sleep and fatigue was analyzed by the entropy of pulse rate variation symbol sequence.Conclusion:the error value of pulse signal variability analysis based on local mean decomposition is small,which can estimate the pulse PRV signal in exercise state,and can promote the detection of clinical PRV signal.

关 键 词：局部均值分解 脉搏信号变异性 序列熵 PIF信号 

分 类 号：R241.1[医药卫生—中医诊断学] TN911.7[医药卫生—中医临床基础]