Literature DB >> 32022886

Characterization of cyclic alternating pattern during sleep in older men and women using large population studies.

Simon Hartmann1, Oliviero Bruni2, Raffaele Ferri3, Susan Redline4, Mathias Baumert1.   

Abstract

STUDY
OBJECTIVES: To assess the microstructural architecture of non-rapid eye movement (NREM) sleep known as cyclic alternating pattern (CAP) in relation to the age, gender, self-reported sleep quality, and the degree of sleep disruption in large community-based cohort studies of older people.
METHODS: We applied a high-performance automated CAP detection system to characterize CAP in 2,811 men from the Osteoporotic Fractures in Men Sleep Study (MrOS) and 426 women from the Study of Osteoporotic Fractures (SOF). CAP was assessed with respect to age and gender and correlated to obstructive apnea-hypopnea index, arousal index (AI-NREM), and periodic limb movements in sleep index. Further, we evaluated CAP across levels of self-reported sleep quality measures using analysis of covariance.
RESULTS: Age was significantly associated with the number of CAP sequences during NREM sleep (MrOS: p = 0.013, SOF = 0.051). CAP correlated significantly with AI-NREM (MrOS: ρ = 0.30, SOF: ρ = 0.29). CAP rate, especially the A2+A3 index, was inversely related to self-reported quality of sleep, independent of age and sleep disturbance measures. Women experienced significantly fewer A1-phases compared to men, in particular, in slow-wave sleep (N3).
CONCLUSIONS: We demonstrate that automated CAP analysis of large-scale databases can lead to new findings on CAP and its subcomponents. We show that sleep disturbance indices are associated with the CAP rate. Further, the CAP rate is significantly linked to subjectively reported sleep quality, independent from traditionally scored markers of sleep fragmentation. Finally, men and women show differences in the microarchitecture of sleep as identified by CAP, despite similar macro-architecture. © Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Entities:  

Keywords:  cyclic alternating pattern; deep learning; sleep disorder breathing; sleep fragmentation; sleep quality; sleep studies

Mesh:

Year:  2020        PMID: 32022886      PMCID: PMC7355398          DOI: 10.1093/sleep/zsaa016

Source DB:  PubMed          Journal:  Sleep        ISSN: 0161-8105            Impact factor:   5.849


  30 in total

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Journal:  Brain Sci       Date:  2021-02-25

Review 5.  Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.

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  5 in total

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