Jacobs Jonathon, Martin Caitlin E, Fuemmeler Bernard, Chen Shanshan
Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA.
Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, Virginia, USA.
J Sleep Res. 2025 Apr;34(2):e14331. doi: 10.1111/jsr.14331. Epub 2024 Sep 17.
Sleep is a complex biological process regulated by networks of neurons and environmental factors. As one falls asleep, neurotransmitters from sleep-wake regulating neurones work in synergy to control the switching of different sleep states throughout the night. As sleep disorders or underlying neuropathology can manifest as irregular switching, analysing these patterns is crucial in sleep medicine and neuroscience. While hypnograms represent the switching of sleep states well, current analyses of hypnograms often rely on oversimplified temporal descriptive statistics (TDS, e.g., total time spent in a sleep state), which miss the opportunity to study the sleep state switching by overlooking the complex structures of hypnograms. In this paper, we propose analysing sleep hypnograms using a seven-state continuous-time Markov model (CTMM). This proposed model leverages the CTMM to depict the time-varying sleep-state transitions, and probes three types of insomnia by distinguishing three types of wake states. Fitting the proposed model to data from 2056 ageing adults in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study, we profiled sleep architectures in this population and identified the various associations between the sleep state transitions and demographic factors and subjective sleep questions. Ageing, sex, and race all show distinctive patterns of sleep state transitions. Furthermore, we also found that the perception of insomnia and restless sleep are significantly associated with critical transitions in the sleep architecture. By incorporating three wake states in a continuous-time Markov model, our proposed method reveals interesting insights into the relationships between objective hypnogram data and subjective sleep quality assessments.
睡眠是一个由神经元网络和环境因素调节的复杂生物过程。当人入睡时，来自睡眠 - 觉醒调节神经元的神经递质协同作用，以控制整夜不同睡眠状态的转换。由于睡眠障碍或潜在的神经病理学可能表现为不规则的转换，因此分析这些模式在睡眠医学和神经科学中至关重要。虽然睡眠图很好地表示了睡眠状态的转换，但目前对睡眠图的分析通常依赖于过于简化的时间描述性统计（TDS，例如在一种睡眠状态下花费的总时间），这通过忽略睡眠图的复杂结构而错失了研究睡眠状态转换的机会。在本文中，我们建议使用七状态连续时间马尔可夫模型（CTMM）来分析睡眠图。该模型利用CTMM来描述随时间变化的睡眠状态转换，并通过区分三种觉醒状态来探究三种类型的失眠。将该模型应用于动脉粥样硬化多族裔研究（MESA）睡眠研究中2056名老年人的数据，我们描绘了该人群的睡眠结构，并确定了睡眠状态转换与人口统计学因素和主观睡眠问题之间的各种关联。年龄、性别和种族都显示出独特的睡眠状态转换模式。此外，我们还发现，对失眠和睡眠不安的感知与睡眠结构中的关键转换显著相关。通过在连续时间马尔可夫模型中纳入三种觉醒状态，我们提出的方法揭示了客观睡眠图数据与主观睡眠质量评估之间关系的有趣见解。
