School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China.
School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
Chaos. 2015 Aug;25(8):083116. doi: 10.1063/1.4929148.
In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.
本文将实验神经生理学记录与统计分析相结合，研究大脑的非线性特征和认知功能。模糊近似熵和模糊样本熵用于描述基于模型的模拟系列和阿尔茨海默病（AD）的脑电图（EEG）系列。首先通过α节律模型生成的模拟 EEG 系列验证了这两种模糊熵的有效性和优势，包括更强的相对一致性和鲁棒性。此外，为了检测 AD 大脑不规则和混沌行为的异常，在 delta、theta、alpha 和 beta 波段中提取基于这两种模糊熵的复杂度特征。结果表明，由于引入了模糊集理论，模糊熵可以更好地区分 AD 的 EEG 信号与正常的 EEG 信号，而近似熵和样本熵则不能。此外，AD 的熵值在 alpha 波段显著降低，特别是在颞叶脑区，如电极 T3 和 T4。此外，模糊样本熵通过支持向量机分类器可以在不同脑区实现更高的组间差异和 88.1%的平均分类准确率。所得结果证明，模糊样本熵可能是一种描述 AD 复杂性异常的有力工具，有助于进一步了解该疾病。
