Shanshan Chen

Sleep is a complex biological process regulated by networks of neurons and environmental factors. As one falls asleep, neurotransmitters from sleep–wake regulating neurons 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, analyzing these patterns is crucial in sleep medicine and neuroscience. Although 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 fail to capture the intricate structure of sleep state switching.

We propose a new method for analyzing sleep hypnogram data. This proposed model leverages the continuous-time Markov model to depict the time-varying sleep-state transitions, and distinguishes between three types of wake states—wake before sleep onset, wake after sleep onset (WASO), and wake after final awakening—to probe three forms of insomnia: difficulty falling asleep, difficulty maintaining sleep, and waking up too early. We fit the proposed model to data from 2056 aging adults in the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study, and profiled sleep architectures in this population and identified the various associations between the sleep state transitions, demographic factors, and subjective sleep questions.

Aging, sex, and race all show distinctive patterns of sleep state transitions. Our analyses confirmed that aging negatively impacts on sleep quality, with older participants showing significantly faster transitions to WASO and slower transitions to deeper sleep states (i.e., N3 and REM). Although men fell asleep significantly faster than women, they experienced greater difficulty transitioning from lighter sleep stages to deep sleep or staying in deep sleep. Compared to White participants, Black participants fell asleep faster than White participants whereas the Hispanic group took longer to fall asleep than White subjects. Additionally, all three minority groups had significantly reduced transition intensity from N2 to N3 stage, indicating a greater challenge in reaching deep sleep compared to White participants. Furthermore, the perception of insomnia and restless sleep are significantly associated with critical transitions in the sleep architecture.

In conclusion, poor sleep quality can manifest in various transition patterns between sleep stages. Our model offers a novel approach to analyzing the time-inhomogeneous, multi-state hypnogram generated by the complex sleep state switching patterns. 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 perceptions of sleep quality. For future sleep studies focusing on insomnia, sleep perceptions, and the links between sleep quality and specific health outcomes, we recommend adopting the seven-state continuous-time Markov model. This model not only reveals meaningful connections to subjective perceptions of sleep quality but also offers rich insights into the complex patterns of sleep state transitions.

Publication: Jacobs, Jonathon, Caitlin E. Martin, Bernard Fuemmeler, and Shanshan Chen. “Profiling the Sleep Architecture of Ageing Adults Using a Seven-State Continuous-Time Markov Model.” Journal of Sleep Research: e14331. https://doi.org/10.1111/jsr.14331.