Şeker Mesut, Özbek Yağmur, Yener Görsev, Özerdem Mehmet Siraç
Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
Department of Neurosciences, Health Science Institute, Dokuz Eylül University, Izmir.
Comput Methods Programs Biomed. 2021 Jul;206:106116. doi: 10.1016/j.cmpb.2021.106116. Epub 2021 Apr 16.
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.
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.
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.
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.
脑电图（EEG）是研究阿尔茨海默病（AD）对人脑影响的最常用筛查工具之一。早期识别AD有助于对痴呆症进行有效治疗。轻度认知障碍（MCI）被视为一个转化阶段。降低脑电图复杂性可作为检测AD的一个指标。本研究的目的是在临床实践中利用脑电图复杂性开发一种三分类诊断方法来检测MCI/AD。本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。
分析了85名AD患者、85名MCI受试者和85名健康对照者在睁眼和闭眼状态下的脑电图记录。计算每个脑电图时段额叶、中央、顶叶、颞叶和枕叶区域的排列熵（PE）值。直观展示PE值的分布情况，以观察MCI/AD与健康对照者之间的差异。将直观观察结果与使用方差分析的统计分析相结合，以确定组间是否存在显著差异。将多项逻辑回归模型应用于特征集，以便对参与者进行个体分类。
测量得到的PE分布表明，AD组的脑电图复杂性较低，健康对照组的较高。由于值的异质性，MCI组表现为中间形式。三分类结果表明，F1分数以及敏感性和特异性率在闭眼状态下的TP8电极处总体判别率最高，达到100%；在睁眼状态下，C3、C4、T8、O2电极处总体判别率最高。从两个患者组中区分出健康对照者的效果最佳。睁眼状态增加了对MCI和AD的区分度。
这项非线性脑电图方法学研究为AD识别提供了高判别率，对相关文献做出了贡献。推荐将PE作为AD研究中一种实用的诊断神经标志物。对于AD诊断，睁眼状态下的静息态脑电图可能比闭眼脑电图记录更具优势。
