Probabilistic sleep architecture models in patients with and without sleep apnea
Open accessAbstract
Summary: Sleep fragmentation of any cause is disruptive to the rejuvenating value of sleep. However, methods to quantify sleep architecture remain limited. We have previously shown that human sleep–wake stage distributions exhibit multi‐exponential dynamics, which are fragmented by obstructive sleep apnea (OSA), suggesting that Markov models may be a useful method to quantify architecture in health and disease. Sleep stage data were obtained from two subsets of the Sleep Heart Health Study database: control subjects with no medications, no OSA, no medical co‐morbidities and no sleepiness (n = 374); and subjects with severe OSA (n = 338). Sleep architecture was simplified into three stages: wake after sleep onset (WASO); non‐rapid eye movement (NREM) sleep; and rapid eye movement (REM) sleep. The connectivity and transition rates among eight ‘generator’ states of a first‐order continuous‐time Markov model were inferred from the observed (‘phenotypic’) distributions: three exponentials each of NREM sleep and WASO; and two exponentials of REM sleep. Ultradian REM cycling was accomplished by imposing time‐variation to REM state entry rates. Fragmentation in subjects with severe OSA involved faster transition probabilities as well as additional state transition paths within the model. The Markov models exhibit two important features of human sleep architecture: multi‐exponential stage dynamics (accounting for observed bout distributions); and probabilistic transitions (an inherent source of variability). In addition, the model quantifies the fragmentation associated with severe OSA. Markov sleep models may prove important for quantifying sleep disruption to provide objective metrics to correlate with endpoints ranging from sleepiness to cardiovascular morbidity.
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Sleep apnoea is associated with significant sleep fragmentation.
“Sleep apnoea is associated with significant sleep fragmentation (Bianchi et al., 2012)”
From Addressing sleep disturbances: an opportunity to prevent cardiometabolic disease? (2014) · cites this paper as Bianchi et al., 2012
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- Authors
- BIANCHI, M. T., EISEMAN, N. A., CASH, S. S., MIETUS, J., PENG, C., & THOMAS, R. J.
- Affiliations
- Harvard University (US)
- Massachusetts General Hospital (US)
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- BIANCHI, MATT T.
- Venue
- Journal of Sleep Research
- Publisher
- Wiley
- Published
- 2011-09-28
- Volume
- 21
- Issue
- 3
- Pages
- 330-341
- ISSN
- 0962-1105 (ISSN-L)
- 1365-2869
- PMC
- PMC4487658
- OpenAlex
- W1534006968
- Cited by
- 44
- References
- 33
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 2 · 2020: 6 · 2019: 4 · 2018: 7 · 2017: 2 · 2016: 4 · 2015: 4 · 2014: 3 · 2013: 1 · 2012: 1
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Sleep Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (n01hc085082-026, n01hc085081-021, n01hc085079-024, n01hc085083-027, n01hc085080-026, n01hc085086-022, n01hc55222-0-0-0)
- National Institute on Aging (R01AG023629)
- National Heart, Lung, and Blood Institute (N01 HC085086, N01 HC085079, N01 HC085083, N01 HC085080, HHSN 268200800007C, HHSN268201200036C, N01HC085082, U01HL080295, N01 HC055222, N01 HC085081)
- Topics
- Health Sciences › Medicine › Physiology › Obstructive Sleep Apnea Research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
- Keywords
- sleep architecture, obstructive sleep apnea, sleep fragmentation, Markov model, sleep stage transitions, REM sleep, NREM sleep, wake after sleep onset, sleep disruption, Sleep Heart Health Study
- MeSH terms
- Computer Simulation, Humans, Models, Theoretical, Probability, Sleep Apnea Syndromes, Sleep Stages, Sleep, REM, Time Factors, Cohort Studies, Polysomnography
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