Ghorbanian Parham, Ramakrishnan Subramanian, Ashrafiuon Hashem
Department of Mechanical Engineering, Center for Nonlinear Dynamics and Control, Villanova University Villanova, PA, USA.
Department of Mechanical and Industrial Engineering, University of Minnesota Duluth Duluth, MN, USA.
Front Comput Neurosci. 2015 Apr 24;9:48. doi: 10.3389/fncom.2015.00048. eCollection 2015.
In this article, the Electroencephalography (EEG) signal of the human brain is modeled as the output of stochastic non-linear coupled oscillator networks. It is shown that EEG signals recorded under different brain states in healthy as well as Alzheimer's disease (AD) patients may be understood as distinct, statistically significant realizations of the model. EEG signals recorded during resting eyes-open (EO) and eyes-closed (EC) resting conditions in a pilot study with AD patients and age-matched healthy control subjects (CTL) are employed. An optimization scheme is then utilized to match the output of the stochastic Duffing-van der Pol double oscillator network with EEG signals recorded during each condition for AD and CTL subjects by selecting the model physical parameters and noise intensity. The selected signal characteristics are power spectral densities in major brain frequency bands Shannon and sample entropies. These measures allow matching of linear time varying frequency content as well as non-linear signal information content and complexity. The main finding of the work is that statistically significant unique models represent the EC and EO conditions for both CTL and AD subjects. However, it is also shown that the inclusion of sample entropy in the optimization process, to match the complexity of the EEG signal, enhances the stochastic non-linear oscillator model performance.
在本文中，人类大脑的脑电图（EEG）信号被建模为随机非线性耦合振荡器网络的输出。结果表明，在健康人和阿尔茨海默病（AD）患者不同脑状态下记录的EEG信号可被理解为该模型的不同的、具有统计学意义的实现。在一项针对AD患者和年龄匹配的健康对照受试者（CTL）的初步研究中，采用了在静息睁眼（EO）和闭眼（EC）静息状态下记录的EEG信号。然后利用一种优化方案，通过选择模型物理参数和噪声强度，将随机杜芬 - 范德波尔双振荡器网络的输出与AD和CTL受试者在每种状态下记录的EEG信号进行匹配。所选的信号特征是主要脑电频段的功率谱密度、香农熵和样本熵。这些测量方法能够匹配线性时变频率内容以及非线性信号信息内容和复杂性。这项工作的主要发现是，具有统计学意义的独特模型代表了CTL和AD受试者的EC和EO状态。然而，研究还表明，在优化过程中纳入样本熵以匹配EEG信号的复杂性，可提高随机非线性振荡器模型的性能。
