心率变异性信号基于小波变换的分解

在对心率变异性（ＨＲＶ）信号及类似的信号进行分析时往往希望把其中具有分形性质的１／ｆ成份和非１／ｆ成份分解开分别处理。然而，由于１／ｆ成份和非１／ｆ成份在频域上相互重叠，不容易简单地用频域滤波的方法来分解。因此有人根据１／ｆ成份的特点提出了所谓粗粒化谱分析的频域分解方法。由于该方法存在一些缺点，本文根据１／ｆ成份的特点提出了基于小波变换的ＨＲＶ信号的时域分解算法。对仿真的ＨＲＶ信号和实际ＨＲＶ信号的分解的结果证实本文提出的方法优于粗粒化谱分析法。

The decomposition of heart rate variability signal based on wavelet transform

It is often desirable to decompose HRV signals into 1/f component and non-1/f component in the analysis of them,because the former is fractal in nature.Since the1/f component and non-1/fcomponent are overlapped in frequency domain,it is difficult to decompose it simply by frequency domain filtering.Yamamoto presented a frequency domain decomposition method called Coarse Graining Spectral Analysis(CGSA).A time domain decomposition method based on Wavelet Transform is presented in this paper which is superior to CGSA.The result of the decomposition of a simulated HRV signal and a real HRV signal confirms our conclusion.