IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0194604.html
   My bibliography  Save this article

Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

Author

Listed:
  • Benjamin D Yetton
  • Elizabeth A McDevitt
  • Nicola Cellini
  • Christian Shelton
  • Sara C Mednick

Abstract

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.

Suggested Citation

  • Benjamin D Yetton & Elizabeth A McDevitt & Nicola Cellini & Christian Shelton & Sara C Mednick, 2018. "Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-27, April.
  • Handle: RePEc:plo:pone00:0194604
    DOI: 10.1371/journal.pone.0194604
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0194604
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0194604&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0194604?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nicola Luigi Bragazzi & Ottavia Guglielmi & Sergio Garbarino, 2019. "SleepOMICS: How Big Data Can Revolutionize Sleep Science," IJERPH, MDPI, vol. 16(2), pages 1-13, January.
    2. Lainey E. Hunnicutt & Makenzie Corgan & Sarah R. Brown & Alyssa Nygaard & George Lesley Meares & Scott R. Collier, 2024. "Sleep Differences in Firefighters: Barracks vs. Home," IJERPH, MDPI, vol. 21(9), pages 1-8, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0194604. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.