Department of Neurology, University Hospital Zurich, Zurich, Switzerland.
PLoS One. 2012;7(11):e48660. doi: 10.1371/journal.pone.0048660. Epub 2012 Nov 7.
Sleep is generally categorized into discrete stages based on characteristic electroencephalogram (EEG) patterns. This traditional approach represents sleep architecture in a static way, but it cannot reflect variations in sleep across time and across the cortex. To investigate these dynamic aspects of sleep, we analyzed sleep recordings in 14 healthy volunteers with a novel, frequency-based EEG analysis. This approach enabled comparison of sleep patterns with low inter-individual variability. We then implemented a new probability dependent, automatic classification of sleep states that agreed closely with conventional manual scoring during consolidated sleep. Furthermore, this analysis revealed a previously unrecognized, interhemispheric oscillation during rapid eye movement (REM) sleep. This quantitative approach provides a new way of examining the dynamic aspects of sleep, shedding new light on the physiology of human sleep.
睡眠通常根据特征性脑电图 (EEG) 模式分为离散阶段。这种传统方法以静态方式表示睡眠结构,但它不能反映睡眠随时间和大脑皮层的变化。为了研究睡眠的这些动态方面,我们对 14 名健康志愿者的睡眠记录进行了一项基于新型频率的 EEG 分析。这种方法可以比较具有低个体间可变性的睡眠模式。然后,我们实现了一种新的概率相关的、自动的睡眠状态分类,该分类在巩固睡眠期间与传统的手动评分非常吻合。此外,这种分析还揭示了 REM 睡眠期间以前未被识别的半球间振荡。这种定量方法为研究睡眠的动态方面提供了一种新的方法,为人类睡眠的生理学提供了新的见解。