知识与财富的链接
目的:将Hilbert-黄变换方法用于压力脉搏波信号的分析,以获得脉搏波信号的时域特征和频率-能量分布。方法:通过经验模态分解（EMD）将脉搏波分解为一组内在模态函数（IMF）,对每个IMF进行 Hilbert变换,获得脉搏波信号幅度和频率的时间分布;由HH谱得到边际谱,反映信号的能量-频率分布;对典型正常个体的脉搏波信号和该个体脉滑变时的脉搏波信号进行处理,比较两种状态下脉搏波信号时-频分布情况。结果:用于实验的两例信号的分析结果显示,脉平信号的HH边际谱与脉滑信号的HH边际谱所表现的能量-频率分布有明显区别,这种区别能被脉平和脉滑变时的心血管活动状态所解释。结论:EMD算法和HHT能较好地用于脉搏波的分析,并且在医学信号处理领域将会有广阔的应用前景。
This paper analyzes the time-frequency feature of pulse wave signal, using Hilbert-Huang Transformation. In the investigation,we used an empirical mode decomposition technique（EMD）, allowing time series of pulse wave signal being decomposed into a small number of intrinsic mode function components（IMF）. Under the Hilbert transformation process, IMF can be translated into an expression called HH spectra,which exhibits the amplitude-frequency-time distribution of the data.The marginal spectra, which present the energy-frequency-time distribution of the data,were obtained by integrating the HH spectra with time. Two pulse signal data sets collected from a subject before and after wine drinking are decomposed,and their time-frequency distributions discussed. We found that two pulse data sets were different in HH spectra.The difference can be explained by the physiological status of the subject''s circulatory system before and after drinking. It is concluded that both EMD and HHT can produce a reliable analysis of pulse wave signal. Both EMD and HHT are promising for applications in medical engineering and bioinformatics.