复杂度域中睡眠脑电图的分析。
Analysis of the sleep EEG in the complexity domain.
作者信息
Mariani Sara, Borges Ana F T, Henriques Teresa, Thomas Robert J, Leistedt Samuel J, Linkowski Paul, Lanquart Jean-Pol, Goldberger Ary L, Costa Madalena D
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:6429-6432. doi: 10.1109/EMBC.2016.7592200.
Conventional sleep analysis relies primarily on electroencephalogram (EEG) waveform features assessed in concert with eye movements, respiration and muscle tone. We explore a complementary "complexity domain" approach based on multiscale entropy (MSE) analysis of EEG signals and discuss its relationships to standard sleep analysis and to that based on electrocardiogram (ECG)-derived cardiopulmonary coupling (CPC). We observe a progressive decrease in complexity associated with decreased arousability, as measured by both conventional sleep scoring and CPC analysis. Furthermore, complexity analysis supports the contention that stage 2 non-REM sleep has distinct sub-phases that map to CPC high- and low-frequency coupled dynamics.
传统的睡眠分析主要依赖于与眼动、呼吸和肌张力协同评估的脑电图（EEG）波形特征。我们探索了一种基于EEG信号多尺度熵（MSE）分析的互补性“复杂性领域”方法，并讨论了其与标准睡眠分析以及基于心电图（ECG）衍生的心肺耦合（CPC）分析之间的关系。我们观察到，通过传统睡眠评分和CPC分析测量，与唤醒能力下降相关的复杂性逐渐降低。此外，复杂性分析支持以下观点，即非快速眼动睡眠第2阶段有不同的子阶段，这些子阶段对应于CPC的高频和低频耦合动态。
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