Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension
Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension
复制标题
DOI：
10.1016/s0013-4694(97)00079-5
复制
发表时间：
1998-03-01
期刊：
影响因子：
--
通讯作者：
Han, SH
中科院分区：
文献类型：
--
作者：
Jeong, J;Kim, SY;Han, SH
我们使用非线性分析来研究阿尔茨海默病患者脑电的动力学特性。我们计算了关联维D2和第一正Lyapunov指数L1。我们使用了Kennel等人提出的一种新方法来计算非线性不变度量。该方法通过观察最近邻域在嵌入维度d从d到d+1变化时的行为来确定合适的最小嵌入维度。我们证明了对于有限的噪声数据，我们的算法比以前的算法更快、更准确。此外，我们发现，在几乎所有渠道，阿尔茨海默病患者的D2和L1值显著低于年龄相近的健康对照组。这些结果表明，与正常健康的大脑相比，阿尔茨海默病患者的大脑表现出不那么混乱的行为。在这篇文章中，我们表明，在复杂的脑动力学中，非线性分析可以为检测传统线性分析无法检测到的相对变化提供一个卓有成效的工具。我们认为，对阿尔茨海默病患者脑电的非线性动态分析将在适当的临床环境下作为一种诊断手段。(C)1998年爱思唯尔爱尔兰科学有限公司。
We used non-linear analysis to investigate the dynamical properties underlying the EEG in patients with Alzheimer's disease. We calculated the correlation dimension D2 and the first positive Lyapunov exponent L1. We employed a new method, which was proposed by Kennel et al., to calculate the non-linear invariant measures. That method determined the proper minimum embedding dimension by looking at the behavior of nearest neighbors under a change in the embedding dimension d from d to d + 1. We demonstrated that for limited noisy data, our algorithm was strikingly faster and more accurate than previous ones. Also, we found that, in almost all channels, patients with Alzheimer's disease had significantly lower D2 and L1 values than those for age-approximated healthy controls. These results suggest that brains afflicted by Alzheimer's disease show behaviors which are less chaotic than those of normal healthy brains. In this paper, we show that non-linear analysis can provide a fruitful tool for detecting relative changes, which cannot be detected by conventional linear analysis, in the complexity of brain dynamics. We propose that non-linear dynamical analyses of the EEGs from patients with Alzheimer's disease will be a diagnostic modality in the appropriate clinical setting. (C) 1998 Elsevier Science Ireland Ltd.