EEG-Based Sleep Staging Analysis with Functional Connectivity.
EEG-Based Sleep Staging Analysis with Functional Connectivity.
复制标题
基于脑电图的睡眠分期分析与功能连接
DOI：
10.3390/s21061988
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发表时间：
2021-03-11
期刊：
影响因子：
--
通讯作者：
Zhu L
中科院分区：
文献类型：
--
作者：
Huang H;Zhang J;Zhu L;Tang J;Lin G;Kong W;Lei X;Zhu L
睡眠分期在睡眠研究中非常重要，因为它是睡眠评估和疾病诊断的基础。相关工作取得了许多可喜的成果。然而，目前的研究大多集中在时域或频域的测量作为分类特征，使用单个或很少的通道，只获得局部特征，而忽略了不同脑区之间的全局信息交换。同时，脑功能连接被认为与脑活动密切相关，可以用来研究脑区之间的相互作用关系。为了从功能连通性的角度探讨睡眠各阶段的脑机制，特别是不同频段的脑功能连通性，采用锁相值（PLV）方法构建功能连通性网络，分析不同频段睡眠各阶段的脑功能相互作用。然后，我们采用特征级、决策级和混合融合方法来讨论不同频段对于睡眠阶段的性能。结果表明：（1）低频带PLV增大（2）在非快速眼动（NREM）的不同阶段，α波对睡眠阶段的区分能力更强;（3）特征级融合的分类精度（6个频段）的融合率分别达到96.91%和96.14%，优于决策层融合和混合融合方法。
Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To explore the electroencephalography (EEG)-based brain mechanisms of sleep stages through functional connectivity, especially from different frequency bands, we applied phase-locked value (PLV) to build the functional connectivity network and analyze the brain interaction during sleep stages for different frequency bands. Then, we performed the feature-level, decision-level and hybrid fusion methods to discuss the performance of different frequency bands for sleep stages. The results show that (1) PLV increases in the lower frequency band (delta and alpha bands) and vice versa during different stages of non-rapid eye movement (NREM); (2) alpha band shows a better discriminative ability for sleeping stages; (3) the classification accuracy of feature-level fusion (six frequency bands) reaches 96.91% and 96.14% for intra-subject and inter-subjects respectively, which outperforms decision-level and hybrid fusion methods.
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