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

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 e

SUBMITTER: Yetton BD 

PROVIDER: S-EPMC5894981 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature