Physiological time series:: distinguishing fractal noises from motions
Physiological time series:: distinguishing fractal noises from motions
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DOI：
10.1007/s004249900135
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发表时间：
2000-02-01
影响因子：
4.5
通讯作者：
Ikrényi, C
中科院分区：
文献类型：
--
作者：
Eke, A;Hermán, P;Ikrényi, C
许多生理信号呈现分形，在其功率谱密度的大范围内具有自相似性。它们类似于两类离散采样的纯分形时间信号之一，分数高斯噪声（fGn）或分数布朗运动（fBM）。fGn级数是fBm级数的元素之间的连续差;它们是平稳的，完全由两个参数sigma（2），方差和H，赫斯特系数表征。这种生理信号的有效表征是有价值的，因为Il定义了时间序列的自相关和分形维数。从傅立叶分析估计H是不准确的，因此需要更鲁棒的方法。频散分析（Disp）适合于噪声信号，而桥式去趋势尺度加窗方差分析（bdSWV）适合于运动信号。其功率谱密度的斜率位于fGn和fBm之间的边界附近的信号难以分类。一种新的信号求和转换方法（SSC），将fGn转换为fBm，或将fBm转换为求和的fBm，然后应用bdSWV，极大地改善了时间序列的分类和（H）over cap（H的估计）的可靠性。将这些方法应用于从麻醉大鼠的大脑皮层获得的激光多普勒血细胞灌注信号，得到A为0.24+/-0.02（SD，n=8），并将信号定义为分数布朗运动。这意味着，流量信号是来自相邻血管的一组局部速度的总和（运动），这些局部速度是负相关的，就像是由局部阻力波动引起的一样。
Many physiological signals appear fractal, in having self-similarity over a large range of their power spectral densities. They are analogous to one of two classes of discretely sampled pure fractal time signals, fractional Gaussian noise (fGn) or fractional Brownian motion (fBm). The fGn series are the successive differences between elements of a fBm series; they are stationary and are completely characterized by two parameters, sigma(2), the variance, and H, the Hurst coefficient. Such efficient characterization of physiological signals Is valuable since Il defines the autocorrelation and the fractal dimension of the time series. Estimation of H from Fourier analysis is inaccurate, so more robust methods are needed. Dispersional analysis (Disp) is good for noise signals while bridge detrended scaled windowed variance analysis (bdSWV) is good for motion signals. Signals whose slopes of their power spectral densities lie near the border between fGn and fBm are difficult to classify. A new method using signal summation conversion (SSC), wherein an fGn is converted to an fBm or an fBm to a summed fBm and bdSWV then applied, greatly improves the classification and the reliability of (H) over cap, the estimates of H, for the times series. Applying these methods to laser-Doppler blood cell perfusion signals obtained from the brain cortex of anesthetized rats gave A of; 0.24+/-0.02 (SD, n=8) and defined the signal as a fractional Brownian motion. The implication is that the flow signal is the summation (motion) of a set of local velocities from neighboring vessels that are negatively correlated, as if induced by local resistance fluctuations.