Afshinpour Babak, Hossein-Zadeh Gholam-Ali, Soltanian-Zadeh Hamid
Control and Intelligent Processing Center of Excellence, Electrical and Computer Engineering Department, Faculty of Engineering, University of Tehran, Tehran, Iran.
J Neurosci Methods. 2008 Jun 30;171(2):340-8. doi: 10.1016/j.jneumeth.2008.03.017. Epub 2008 Apr 4.
Unknown low frequency fluctuations called "trend" are observed in noisy time-series measured for different applications. In some disciplines, they carry primary information while in other fields such as functional magnetic resonance imaging (fMRI) they carry nuisance effects. In all cases, however, it is necessary to estimate them accurately. In this paper, a method for estimating trend in the presence of fractal noise is proposed and applied to fMRI time-series. To this end, a partly linear model (PLM) is fitted to each time-series. The parametric and nonparametric parts of PLM are considered as contributions of hemodynamic response and trend, respectively. Using the whitening property of wavelet transform, the unknown components of the model are estimated in the wavelet domain. The results of the proposed method are compared to those of other parametric trend-removal approaches such as spline and polynomial models. It is shown that the proposed method improves activation detection and decreases variance of the estimated parameters relative to the other methods.
在针对不同应用测量的噪声时间序列中，观察到了被称为“趋势”的未知低频波动。在某些学科中，它们携带主要信息，而在其他领域，如功能磁共振成像（fMRI）中，它们携带干扰效应。然而，在所有情况下，准确估计它们都是必要的。本文提出了一种在存在分形噪声的情况下估计趋势的方法，并将其应用于fMRI时间序列。为此，对每个时间序列拟合一个部分线性模型（PLM）。PLM的参数部分和非参数部分分别被视为血液动力学响应和趋势的贡献。利用小波变换的白化特性，在小波域中估计模型的未知分量。将所提出方法的结果与其他参数化趋势去除方法（如样条和多项式模型）的结果进行比较。结果表明，相对于其他方法，所提出的方法改善了激活检测并降低了估计参数的方差。
