Department of Electrical and Electronics Engineering, Dicle University, Diyarbakir, Turkey.
J Neurosci Methods. 2024 Sep;409:110216. doi: 10.1016/j.jneumeth.2024.110216. Epub 2024 Jul 2.
Neurological disorders arise primarily from the dysfunction of brain cells, leading to various impairments. Electroencephalography (EEG) stands out as the most popular method in the discovery of neuromarkers indicating neurological disorders. The proposed study investigates the effectiveness of spectral and synchrony neuromarkers derived from resting state EEG in the detection of Mild Cognitive Impairment (MCI) with controls.
The dataset is composed of 10 MCI and 10 HC groups. Spectral features and synchrony measures are utilized to detect slowing patterns in MCI. Efficient neuro-markers are classified by 25 classification algorithm. Independent samples t-test and Pearson's Correlation Coefficients are applied to reveal group differences for spectral markers, and repeated measures ANOVA is tested for wPLI-based markers.
Lower peak amplitudes are prominent in MCI participants for high frequencies indicating slower physiological behavior of the demented EEG. The MCI and HC groups are correctly classified with 95 % acc. using peak amplitudes of beta band with LGBM classifier. Higher wPLI values are calculated for HC participants in high frequencies. The alpha wPLI values achieve a classification accuracy of 99 % using the LGBM algorithm for MCI detection.
The neuro-markers including peak amplitudes, frequencies, and wPLIs with advanced machine learning techniques showcases the innovative nature of this research.
The findings suggest that peak amplitudes and wPLI in high frequency bands derived from resting state EEG are effective neuromarkers for detection of MCI. Spectral and synchrony neuro-markers hold great promise for accurate MCI detection.
神经系统疾病主要源于脑细胞功能障碍，导致各种损伤。脑电图（EEG）是发现提示神经障碍的神经生物标志物的最常用方法。本研究旨在探讨静息态 EEG 的频谱和同步神经生物标志物在识别轻度认知障碍（MCI）与对照组中的有效性。
该数据集由 10 名 MCI 和 10 名 HC 组成。采用频谱特征和同步测量来检测 MCI 的减速模式。使用 25 种分类算法对有效神经生物标志物进行分类。采用独立样本 t 检验和 Pearson 相关系数揭示频谱生物标志物的组间差异，采用重复测量方差分析测试基于 wPLI 的生物标志物。
MCI 组高频段的峰值振幅较低，表明痴呆 EEG 的生理行为较慢。使用 LGBM 分类器对 beta 波段的峰值振幅进行分类，MCI 和 HC 组的正确分类率为 95%。高频段的 HC 参与者的 wPLI 值较高。使用 LGBM 算法，alpha wPLI 值对 MCI 的检测准确率为 99%。
包括峰值振幅、频率和 wPLI 的神经生物标志物与先进的机器学习技术相结合，展示了这项研究的创新性。
研究结果表明，静息态 EEG 高频段的峰值振幅和 wPLI 是识别 MCI 的有效神经生物标志物。频谱和同步神经生物标志物具有准确识别 MCI 的巨大潜力。
