Adaptive Fractal Analysis Reveals Limits to Fractal Scaling in Center of Pressure Trajectories
Adaptive Fractal Analysis Reveals Limits to Fractal Scaling in Center of Pressure Trajectories
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DOI：
10.1007/s10439-012-0646-9
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发表时间：
2013-08-01
影响因子：
3.8
通讯作者：
Riley, Michael A.
中科院分区：
文献类型：
--
作者：
Kuznetsov, Nikita;Bonnette, Scott;Riley, Michael A.
分形时间序列分析方法通常用于分析压力中心（COP）信号，其目的是揭示直立站立控制的潜在神经肌肉过程。分形方法的使用通常与假设COP是分数高斯噪声（fGn）或分数布朗运动（fBm）的实例相结合。我们的目的是评估的fGn-fBm框架的适用性COP的COP信号揭示了一种新的方法，自适应分形分析（AFA）的几个特点。AFA量化了全局平滑趋势信号拟合的残差方差如何随执行拟合的时间尺度而缩放。应用AFA COP信号显示，有可能是三个分形标度区域的COP，而不是一个预期从一个纯的fGn或fBm过程。最快标度下的标度区域是反持续的，跨度类似于30-90 ms，中间标度区域是持续的，跨度类似于200 ms-1.9 s，最慢标度区域是反持续的，跨度类似于5-40 s。中间分形标度区域是定义最清楚的，但它只贡献了COP信号总光谱能量的11%左右，这表明COP信号的其他特征对整体动力学的贡献更重要。此外，超过一半的赫斯特指数估计的中间区域大于理论预期的范围[0，1]的fGn-fBm过程。这些结果表明fGn-fBm框架不适合用于建模COP信号。ON-OFF不稳定性可能为COP提供更好的建模框架，多尺度方法可能更适合分析COP数据。
Fractal time series analysis methods are commonly used for analyzing center of pressure (COP) signals with the goal of revealing the underlying neuromuscular processes for upright stance control. The use of fractal methods is often coupled with the assumption that the COP is an instance of fractional Gaussian noise (fGn) or fractional Brownian motion (fBm). Our purpose was to evaluate the applicability of the fGn-fBm framework to the COP in light of several characteristics of COP signals revealed by a new method, adaptive fractal analysis (AFA). AFA quantifies how the variance of the residuals to fits of a globally smooth trend signal scales with the time scale at which the fits are performed. Application of AFA to COP signals revealed that there are potentially three fractal scaling regions in the COP as opposed to one as expected from a pure fGn or fBm process. The scaling region at the fastest scale was anti-persistent and spanned similar to 30-90 ms, the intermediate was persistent and spanned similar to 200 ms-1.9 s, and the slowest was anti-persistent and spanned similar to 5-40 s. The intermediate fractal scaling region was the most clearly defined, but it only contributed around 11% of the total spectral energy of the COP signal, indicating that other features of the COP signal contribute more importantly to the overall dynamics. Also, more than half of the Hurst exponents estimated for the intermediate region were greater than the theoretically expected range [0,1] for fGn-fBm processes. These results suggest the fGn-fBm framework is not appropriate for modeling COP signals. ON-OFF intermittency might provide a better modeling framework for the COP, and multiscale approaches may be more appropriate for analyzing COP data.