Deng Bin, Liang Li, Li Shunan, Wang Ruofan, Yu Haitao, Wang Jiang, Wei Xile
School of Electrical Engineering and Automation, Tianjin University, Tianjin, China.
Chaos. 2015 Apr;25(4):043105. doi: 10.1063/1.4917013.
In this paper, weighted-permutation entropy (WPE) is applied to investigating the complexity abnormalities of Alzheimer's disease (AD) by analyzing 16-channel electroencephalograph (EEG) signals from 14 severe AD patients and 14 age-matched normal subjects. The WPE values are estimated in the delta, the theta, the alpha, and the beta sub-bands for each channel with an overlapped sliding window. WPE is modified from the permutation entropy (PE), which has been recently suggested as a measurement to extract the complexity of the EEG signals. The advantage of WPE over PE is verified by both the model simulated and the experimental EEG signals. Although the results show that both the average PE and WPE of AD patients are decreased in contrast with the normal group in these four sub-bands, especially in the theta band, WPE can exhibit a better performance in distinguishing the AD patients from the normal controls by the more significant differences in the four sub-bands, which may be attributed to the brain dysfunction. Thus, it suggests that WPE may become a probable useful tool to detect brain dysfunction in AD and it seems to be promising to disclose the abnormalities of brain activity for other neural disease.
在本文中，加权排列熵（WPE）被应用于通过分析14名重度阿尔茨海默病（AD）患者和14名年龄匹配的正常受试者的16通道脑电图（EEG）信号来研究AD的复杂性异常。使用重叠滑动窗口对每个通道的δ、θ、α和β子带中的WPE值进行估计。WPE是从排列熵（PE）修改而来的，排列熵最近被建议作为一种提取EEG信号复杂性的测量方法。通过模型模拟和实验EEG信号都验证了WPE相对于PE的优势。虽然结果表明，在这四个子带中，与正常组相比，AD患者的平均PE和WPE均降低，尤其是在θ带，但WPE在区分AD患者和正常对照方面表现出更好的性能，因为在四个子带中差异更显著，这可能归因于脑功能障碍。因此，这表明WPE可能成为检测AD脑功能障碍的一种可能有用的工具，并且似乎有望揭示其他神经疾病的脑活动异常。
