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

• Published in: PloS one Vol. 13; no. 4; p. e0194604
• Main Authors: Yetton, Benjamin D, McDevitt, Elizabeth A, Cellini, Nicola, Shelton, Christian, Mednick, Sara C
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11.04.2018; Public Library of Science (PLoS)
• Subjects: Adults; Age; Age Factors; Aged; Analysis; Architecture; Bayes Theorem; Bayesian analysis; Biological clocks; Biology and Life Sciences; Body mass; Body Mass Index; Body size; Computer and Information Sciences; Data management; Eye movements; Female; Humans; Individuality; Male; Males; Medicine and Health Sciences; Middle Aged; NREM sleep; Older people; People and Places; Physical Sciences; Polysomnography; Probability; Regression Analysis; Sex; Sex Factors; Sleep; Sleep - physiology; Sleep and wakefulness; Sleep apnea; Sleep Apnea Syndromes; Sleep disorders; Sleep Stages; Time Factors; Time of use; Transition probabilities; Transitions; United States > US; Israel; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0194604

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.