Dynamics of sleep: Exploring critical transitions and early warning signals
Dynamics of sleep: Exploring critical transitions and early warning signals
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DOI：
10.1016/j.cmpb.2020.105448
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发表时间：
2020-09-01
影响因子：
6.1
通讯作者：
van der Maas, Han L. J.
中科院分区：
文献类型：
--
作者：
de Mooij, Susanne M. M.;Blanken, Tessa F.;van der Maas, Han L. J.
背景和目的：在标准实践中，人类观察者根据AASM手册中规定的特定规则将睡眠分为不同的阶段。为了发展新的睡眠阶段经验标准，我们证明了将睡眠阶段概念化为非线性动力系统中的吸引子状态的原理。方法：使用两名健康睡眠参与者的脑电(单通道)来验证这一概念。首先，选择不同的脑电历元，分别用MLR分类器和人工评分来检测。其次，利用变点分析来识别脑电信号的突变。结果：在脑电信号中识别出多个变化点，主要与氮气相互作用。这些变化之前的动态变化揭示了部分变化点的一般性预警信号指标，以及复杂系统(如生态系统、气候、癫痫发作、全球金融系统)的特征。结论：勾勒出的研究睡眠脑电关键转变的新框架可能有助于理解睡眠阶段转变动力学中的个体差异和病理差异。将睡眠形式化为非线性动态系统对于定义睡眠质量(即平衡状态的稳定性和可达性)和扰乱睡眠(即在不稳定的睡眠状态之间不断转换)是有用的。(C)2020作者。爱思唯尔出版公司(Elsevier B.V.)
Background and objectives: In standard practice, sleep is classified into distinct stages by human observers according to specific rules as for instance specified in the AASM manual. We here show proof of principle for a conceptualization of sleep stages as attractor states in a nonlinear dynamical system in order to develop new empirical criteria for sleep stages.Methods: EEG (single channel) of two healthy sleeping participants was used to demonstrate this conceptualization. Firstly, distinct EEG epochs were selected, both detected by a MLR classifier and through manual scoring. Secondly, change point analysis was used to identify abrupt changes in the EEG signal. Thirdly, these detected change points were evaluated on whether they were preceded by early warning signals.Results: Multiple change points were identified in the EEG signal, mostly in interplay with N2. The dynamics before these changes revealed, for a part of the change points, indicators of generic early warning signals, characteristic of complex systems (e.g., ecosystems, climate, epileptic seizures, global finance systems).Conclusions: The sketched new framework for studying critical transitions in sleep EEG might benefit the understanding of individual and pathological differences in the dynamics of sleep stage transitions. Formalising sleep as a nonlinear dynamical system can be useful for definitions of sleep quality, i.e. stability and accessibility of an equilibrium state, and disrupted sleep, i.e. constant shifting between instable sleep states. (C) 2020 The Authors. Published by Elsevier B.V.