Background and objective

Electroencephalogram (EEG) is one of the most demanded screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain. Identification of AD in early stage gives rise to efficient treatment in dementia. Mild Cognitive Impairment (MCI) is considered as a conversion stage. Reducing EEG complexity can be used as a marker to detect AD. The aim of this study is to develop a 3-way diagnostic classification using EEG complexity in the detection of MCI/AD in clinical practice. This study also investigates the effects of different eyes states, i.e. eyes-open, eyes-closed on classification performance.

Methods

EEG recordings from 85 AD, 85 MCI subjects, and 85 Healthy Controls with eyes-open and eyes- closed are analyzed. Permutation Entropy (PE) values are computed from frontal, central, parietal, temporal, and occipital regions for each EEG epoch. Distribution of PE values are visualized to observe discrimination of MCI/AD with HC. Visual investigations are combined with statistical analysis using ANOVA to determine whether groups are significant or not. Multinomial Logistic Regression model is applied to feature sets in order to classify participants individually.

Results

Distribution of measured PE shows that EEG complexity is lower in AD and higher in HC group. MCI group is observed as an intermediate form due to heterogeneous values. Results from 3-way classification indicate that F1-scores and rates of sensitivity and specificity achieve the highest overall discrimination rates reaching up to 100% for at TP8 for eyes-closed condition; and C3, C4, T8, O2 electrodes for eyes-open condition. Classification of HC from both patient groups is achieved best. Eyes-open state increases discrimination of MCI and AD.

Conclusions

This nonlinear EEG methodology study contributes to literature with high discrimination rates for identification of AD. PE is recommended as a practical diagnostic neuro-marker for AD studies. Resting state EEG at eyes-open condition can be more advantageous over eyes-closed EEG recordings for diagnosis of AD.

中文翻译：

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脑电动力学复杂性的置换熵神经标志物用于阿尔茨海默氏病的早期诊断

背景和目标

脑电图（EEG）是研究阿尔茨海默氏病（AD）对人脑的影响最需要的筛查工具之一。早期识别AD可提高对痴呆症的有效治疗。轻度认知障碍（MCI）被认为是转换阶段。降低脑电图的复杂性可以用作检测AD的标记。这项研究的目的是在临床实践中使用脑电图复杂性检测MCI / AD来建立三向诊断分类。这项研究还调查了不同眼睛状态（即睁眼，闭眼）对分类性能的影响。

方法

分析了来自85位AD，85位MCI受试者和85位眼睛睁开和闭眼的健康对照者的脑电图记录。从每个EEG时期的额叶，中央，顶叶，颞叶和枕叶区域计算置换熵（PE）值。PE值的分布被可视化以观察MCI / AD与HC的区别。视觉调查与使用ANOVA进行的统计分析相结合，以确定组是否重要。多项式Lo​​gistic回归模型应用于特征集，以便分别对参与者进行分类。

结果

测得的PE的分布表明，AD组的EEG复杂度较低，HC组的较高。由于异质性值，MCI基团被观察为中间形式。三向分类的结果表明，在闭眼条件下，TP8的F1得分以及敏感性和特异性的比率达到最高的总体辨别率，最高可达100％。C3，C4，T8，O2电极用于睁眼。两组患者的HC分类均达到最佳。睁大眼睛的状态增加了对MCI和AD的辨别力。

结论

这项非线性脑电图方法学研究为识别AD的文献提供了很高的判别率。建议将PE用作AD研究的实用诊断神经标记。睁眼时静息状态的脑电图比闭眼时脑电图记录对AD的诊断更为有利。