title: A probabilistic framework for a physiological representation of dynamically evolving sleep state.
abstract: This work presents a probabilistic method for mapping human sleep electroencephalogram (EEG) signals onto a state space based on a biologically plausible mathematical model of the cortex. From a noninvasive EEG signal, this method produces physiologically meaningful pathways of the cortical state over a night of sleep. We propose ways in which these pathways offer insights into sleep-related conditions, functions, and complex pathologies. To address explicitly the noisiness of the EEG signal and the stochastic nature of the mathematical model, we use a probabilistic Bayesian framework to map each EEG epoch to a distribution of likelihoods over all model sleep states. We show that the mapping produced from human data robustly separates rapid eye movement sleep (REM) from slow wave sleep (SWS). A Hidden Markov Model (HMM) is incorporated to improve the path results using the prior knowledge that cortical physiology has temporal continuity.
authors.affiliation: Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA, vdadok@berkeley.edu.
authors.forename: Vera M
authors.initials: VM
authors.lastname: Dadok
authors.forename: Heidi E
authors.initials: HE
authors.lastname: Kirsch
authors.forename: Jamie W
authors.initials: JW
authors.lastname: Sleigh
authors.forename: Beth A
authors.initials: BA
authors.lastname: Lopour
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Szeri
mesh_terms: D001499:Bayes Theorem; D001931:Brain Mapping; D058256:Brain Waves / Q000502:physiology*; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography; D005133:Eye Movements; D006801:Humans; D008954:Models, Biological; D017711:Nonlinear Dynamics; D012894:Sleep Stages / Q000502:physiology*