Li Yanjun, Tang Xiaoying, Xu Zhi, Liu Weifeng, Li Jing
School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, 100094, China.
Australas Phys Eng Sci Med. 2016 Mar;39(1):147-55. doi: 10.1007/s13246-015-0409-7. Epub 2016 Mar 2.
Whether the temporal correlation between inter-leads Electroencephalogram (EEG) that located on the boundary between left and right brain hemispheres is associated with sleep stages or not is still unknown. The purpose of this paper is to evaluate the role of correlation coefficients between EEG leads Fpz-Cz and Pz-Oz for automatic classification of sleep stages. A total number of 39 EEG recordings (about 20 h each) were selected from the expanded sleep database in European data format for temporal correlation analysis. Original waveform of EEG was decomposed into sub-bands δ (1-4 Hz), θ (4-8 Hz), α (8-13 Hz) and β (13-30 Hz). The correlation coefficient between original EEG leads Fpz-Cz and Pz-Oz within frequency band 0.5-30 Hz was defined as r(EEG) and was calculated every 30 s, while that between the two leads EEG in sub-bands δ, θ, α and β were defined as r(δ), r(θ), r(α) and r(β), respectively. Classification of wakefulness and sleep was processed by fixed threshold that derived from the probability density function of correlation coefficients. There was no correlation between EEG leads Fpz-Cz and Pz-Oz during wakefulness (|r| < 0.1 for r(θ), r(α) and r(β), while 0.3 > r > 0.1 for r(EEG) and r(δ)), while low correlation existed during sleep (r ≈ -0.4 for r(EEG), r(δ), r(θ), r(α) and r(β)). There were significant differences (analysis of variance, P < 0.001) for r(EEG), r(δ), r(θ), r(α) and r(β) during sleep when in comparison with that during wakefulness, respectively. The accuracy for distinguishing states between wakefulness and sleep was 94.2, 93.4, 89.4, 85.2 and 91.4% in terms of r(EEG), r(δ), r(θ), r(α) and r(β), respectively. However, no correlation index between EEG leads Fpz-Cz and Pz-Oz could distinguish all five types of wakefulness, rapid eye movement (REM) sleep, N1 sleep, N2 sleep and N3 sleep. In conclusion, the temporal correlation between EEG bipolar leads Fpz-Cz and Pz-Oz are highly associated with sleep-wake stages. Moreover, high accuracy of sleep-wake classification could be achieved by the temporal correlation within frequency band 0.5-30 Hz between EEG leads Fpz-Cz and Pz-Oz.
位于左右脑半球边界的导联间脑电图(EEG)的时间相关性是否与睡眠阶段相关仍不清楚。本文的目的是评估EEG导联Fpz-Cz和Pz-Oz之间的相关系数在睡眠阶段自动分类中的作用。从扩展的欧洲数据格式睡眠数据库中选取了39份EEG记录(每份约20小时)进行时间相关性分析。EEG的原始波形被分解为δ(1-4Hz)、θ(4-8Hz)、α(8-13Hz)和β(13-30Hz)子带。将0.5-30Hz频段内原始EEG导联Fpz-Cz和Pz-Oz之间的相关系数定义为r(EEG),每30秒计算一次,而子带δ、θ、α和β中两个导联EEG之间的相关系数分别定义为r(δ)、r(θ)、r(α)和r(β)。通过从相关系数的概率密度函数导出的固定阈值对清醒和睡眠进行分类。清醒时EEG导联Fpz-Cz和Pz-Oz之间无相关性(r(θ)、r(α)和r(β)时|r| < 0.1,r(EEG)和r(δ)时0.3 > r > 0.1),而睡眠时存在低相关性(r(EEG)、r(δ)、r(θ)、r(α)和r(β)时r ≈ -0.4)。与清醒时相比,睡眠时r(EEG)、r(δ)、r(θ)、r(α)和r(β)存在显著差异(方差分析,P < 0.001)。根据r(EEG)、r(δ)、r(θ)、r(α)和r(β)区分清醒和睡眠状态的准确率分别为94.2%、93.4%、89.4%、85.2%和91.4%。然而,EEG导联Fpz-Cz和Pz-Oz之间的相关指数无法区分所有五种清醒、快速眼动(REM)睡眠、N1睡眠、N2睡眠和N3睡眠类型。总之,EEG双极导联Fpz-Cz和Pz-Oz之间的时间相关性与睡眠-清醒阶段高度相关。此外,通过EEG导联Fpz-Cz和Pz-Oz之间0.5-30Hz频段内的时间相关性可以实现较高的睡眠-清醒分类准确率。