Sleep EEG analysis utilizing inter-channel covariance matrices
Sleep EEG analysis utilizing inter-channel covariance matrices
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DOI：
10.1016/j.bbe.2020.01.013
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发表时间：
2020-01-01
影响因子：
6.4
通讯作者：
Sinha, Neelam
中科院分区：
文献类型：
--
作者：
Gopan, Gopika K.;Prabhu, Sathvik S.;Sinha, Neelam
背景：睡眠对正常的身体功能至关重要，因为睡眠障碍会对人产生不利影响。脑电图（EEG）信号指示大脑功能，并具有不同睡眠阶段的特征签名。这些使脑电图成为深入研究睡眠的有效工具。睡眠阶段大致分为快速眼动（REM）和非快速眼动（NREM）。NREM分为三个阶段。这项工作的目的是区分清醒，NREM 1，NREM 2，NREM 3和REM的给定的EEG时期。这里使用了包含5个EEG通道的DREAMS受试者数据库。本研究的重点是利用脑电在睡眠过程中不同脑区的相互依赖性的变化。新的方法：利用小波分解通道的协方差矩阵来获得相互依赖性的变化。这些功能集包括：（1）简单矩阵性质（MF），如迹、行列式和范数，（2）特征值（E1），（3）对应于最大特征值的特征向量（E2）和（4）使用黎曼几何获得的切向量（RG-TS）。将特征输入到具有装袋的集成分类器。采用了特定主题、所有主题合并和留一主题的分析方法。结果如下：在所有的分析方法中，RG-TS特征的准确率最高（80.05%、83.05%和61.79%），紧随其后的是E1（79.49%、77.14%和58.34%）。这项工作还确保了分类器不会由于类分布不均匀而产生偏差。结论：RG-TS和E1特征的表现表明，前额叶和枕叶沿着与中央叶的相互依赖性的变化可以用来区分不同的睡眠阶段。(c)2020波兰科学院纳莱茨生物控制学和生物医学工程研究所。Elsevier B. V.出版，保留所有权利。
Background: Sleep is vital for normal body functions as sleep disorders can adversely affect a person. Electroencephalographic (EEG) signals indicate brain functions and have characteristic signatures for various sleep stages. These enable the use of EEG as an effective tool for in-depth studies about sleep. Sleep stages are broadly divided as rapid eye movement (REM) and non-rapid eye movement (NREM). NREM is further divided into 3 stages. The objective of the work is to distinguish the given EEG epoch as wake, NREM1, NREM2, NREM3 and REM. DREAMS Subject Database containing 5 EEG channels is used here. This work focuses on utilizing EEG by exploiting variations in inter-dependencies of different brain regions during sleep.New method: Covariance matrices of the wavelet-decomposed channels are used to obtain the variations in inter-dependencies. The feature sets are: (1) simple matrix properties(MF) like trace, determinant and norm, (2) eigen-values (E1), (3) eigen- vector corresponding to the largest eigen-value (E2) and (4) tangent vectors obtained using Riemann geometry (RG-TS). The features are input to ensemble classifier with bagging. Subject-specific, All-subjects-combined and Leave-one-subject-out methods of analysis are carried out. Results: In all methods of analysis, RG-TS features give maximum accuracy (80.05%, 83.05% and 61.79%), closely followed by E1 (79.49%, 77.14% and 58.34%).Comparison with existing method: The proposed method obtains higher and/or comparable accuracy. This work also ensures no biasing of classifier due to unequal class distribution. Conclusion: The performances of RG-TS and E1 features reveal that the changes in interdependencies of pre-frontal and occipital lobe along with the central lobe can be used to distinguish the different sleep stages. (c) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.