Garcia-Molina Gary, Vissapragada Sreeram, Mahadevan Anandi, Goodpaster Robert, Riedner Brady, Bellesi Michele, Tononi Giulio
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2834-2838. doi: 10.1109/EMBC.2016.7591320.
The quantification of sleep architecture has high clinical value for diagnostic purposes. While the clinical standard to assess sleep architecture is in-lab based polysomnography, higher ecological validity can be obtained with multiple sleep recordings at home. In this paper, we use a dataset composed of fifty sleep EEG recordings at home (10 per study participant for five participants) to analyze the sleep stage transition dynamics using Markov chain based modeling. The statistical analysis of the duration of continuous sleep stage bouts is also analyzed to identify the speed of transition between sleep stages. This analysis identified two types of NREM states characterized by fast and slow exit rates which from the EEG analysis appear to correspond to shallow and deep sleep respectively.
睡眠结构的量化对于诊断目的具有很高的临床价值。虽然评估睡眠结构的临床标准是基于实验室的多导睡眠图，但在家中进行多次睡眠记录可获得更高的生态效度。在本文中，我们使用了一个由五十次在家睡眠脑电图记录组成的数据集（五名参与者，每人10次），通过基于马尔可夫链的建模来分析睡眠阶段转换动态。还对连续睡眠阶段持续时间进行了统计分析，以确定睡眠阶段之间的转换速度。该分析确定了两种以快速和慢速退出率为特征的非快速眼动睡眠状态，从脑电图分析来看，它们似乎分别对应浅睡眠和深睡眠。
