Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
METHODS: The neuro-markers including peak amplitudes, frequencies, and wPLIs with advanced machine learning techniques showcases the innovative nature of this research.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
METHODS: The neuro-markers including peak amplitudes, frequencies, and wPLIs with advanced machine learning techniques showcases the innovative nature of this research.
CONCLUSIONS: 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.
摘要：
背景：神经系统疾病主要由脑细胞功能障碍引起，导致各种损伤。脑电图（EEG）是发现表明神经系统疾病的神经标记物的最流行方法。拟议的研究调查了源自静息状态EEG的光谱和同步神经标记物在对照检测轻度认知障碍（MCI）中的有效性。
方法：数据集由10个MCI和10个HC组组成。利用光谱特征和同步性度量来检测MCI中的减慢模式。通过分类算法对有效的神经标记进行分类。独立样本t检验和皮尔逊相关系数用于揭示光谱标记的群体差异，和重复测量方差分析测试基于wPLI的标志物。
结果：对于高频率，MCI参与者的峰值振幅较低，表明痴呆脑电图的生理行为较慢。MCI和HC组被正确地分类为95%acc。用LGBM分类器使用β波段的峰值振幅。在高频中为HC参与者计算更高的wPLI值。使用用于MCI检测的LGBM算法,alphawPLI值实现99%的分类准确度。
■神经标记包括峰值振幅，频率,具有先进机器学习技术的wPLI展示了这项研究的创新性。
结论：研究结果表明，静息状态EEG的高频带中的峰值幅度和wPLI是检测MCI的有效神经标志物。光谱和同步神经标记物对于准确的MCI检测具有很大的前景。
方法：数据集由10个MCI和10个HC组组成。利用光谱特征和同步性度量来检测MCI中的减慢模式。通过分类算法对有效的神经标记进行分类。独立样本t检验和皮尔逊相关系数用于揭示光谱标记的群体差异，和重复测量方差分析测试基于wPLI的标志物。
结果：对于高频率，MCI参与者的峰值振幅较低，表明痴呆脑电图的生理行为较慢。MCI和HC组被正确地分类为95%acc。用LGBM分类器使用β波段的峰值振幅。在高频中为HC参与者计算更高的wPLI值。使用用于MCI检测的LGBM算法,alphawPLI值实现99%的分类准确度。
■神经标记包括峰值振幅，频率,具有先进机器学习技术的wPLI展示了这项研究的创新性。
结论：研究结果表明，静息状态EEG的高频带中的峰值幅度和wPLI是检测MCI的有效神经标志物。光谱和同步神经标记物对于准确的MCI检测具有很大的前景。
