Tracking the Sleep Onset Process: An Empirical Model of Behavioral and Physiological Dynamics

Michael J. Prerau, Katie E. Hartnack, Gabriel Obregon-Henao, Aaron L. Sampson, Margaret Merlino, Karen Gannon, Matt T. Bianchi, Jeffrey M. Ellenbogen, Patrick L. Purdon

Abstract: Author Summary How can we tell when someone has fallen asleep? Understanding the way we fall asleep is an important problem in sleep medicine, since sleep disorders can disrupt the process of falling asleep. In the case of insomnia, subjects may fall asleep too slowly, whereas during sleep deprivation or narcolepsy, subjects fall asleep too quickly. Current methods for tracking the wake/sleep transition are time-consuming, subjective, and simplify the sleep onset process in a way that severely limits the accuracy, power, and scope of any resulting clinical metrics. In this paper, we describe a new physiologically principled method that dynamically combines information from brainwaves, muscle activity, and a novel minimally-disruptive behavioral task, to automatically create a continuous dynamic characterization of a person's state of wakefulness. We apply this method to a cohort of healthy subjects, successfully tracking the changes in wakefulness as the subjects fall asleep. This analysis reveals and statistically quantifies a subset of subjects who still respond to behavioral stimuli even though their brain would appear to be asleep by clinical measures. By developing an automated tool to precisely track the wake/sleep transition, we can better characterize and diagnose sleep disorders, and more precisely measure the effect of sleep medications.