为了克服时域提取脉率变异性(PRV)信号过程中噪声、采样频率等因素的影响,提出一种PRV估计的新方法—局部均值分解(LMD)法。首先对原始脉搏信号进行LMD分解及Hilbert变换, 得到脉搏信号各积函数(PF)分量、脉搏信号瞬时频率(PIF)以及边际谱, 按照PRV信号频率分布得到瞬时脉率(IPR)信号。分别利用LMD估计IPR信号的方法和时域提取PRV信号的方法对本研究采集的10名大学生脉搏信号同时进行处理, 经对比发现IPR信号可以准确地表征PRV信号。利用LMD法估计睡眠、视觉疲劳、运动等状态下脉搏信号中的PRV信号, 结果表明该方法可用于估计不同状态下脉搏信号中的PRV信号；对MIT-BIH数据库中的年轻人和老年人的PRV信号进行短时脉率变异符号序列熵分析, 结果表明该方法可以敏感地检测出年龄的变化。本文工作为临床PRV信号的有效检测和处理提供了一种有效的方法。

In order to overcome the influence of noise and sampling frequency in the process of extracting Pulse Rate Variability(PRV) signal in time domain, a new method of PRV estimation is proposed―Local Mean Decomposition(LMD). Firstly, LMD decomposition and Hilbert transform are performed to the original pulse signal, and each Product Function(PF) component, Pulse signal Instantaneous Frequency(PIF) and marginal spectrum of the pulse signal are obtained. Then the Instantaneous Pulse Rate(IPR) signal is obtained according to the PRV signal frequency distribution. By using the method of estimating IPR signal on LMD and extracting PRV signal in time domain, the pulse signals of 10 college students collected in this study are processed at the same time. It is found that the IPR signal can accurately characterize the PRV signal. LMD method is adopted to estimate the PRV signal in the pulse signal of sleep, visual fatigue and motion state, and the results show that the method can be applied to estimate the PRV signal in different pulse signals. The PRV signals of the young and the old in the MIT-BIH database are analyzed on the entropy of the symbol sequence of short pulse rate. The results show that the method can detect the change of the age. This work provides a method for the effective detection and processing of PRV signals.