A probabilistic framework for a physiological representation of dynamically evolving sleep state
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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.
KeywordsSleep Cortex Acetylcholine Adenosine
This work was partially supported by an NSF Graduate Research Fellowship to Vera Dadok and in part by the National Science Foundation through the research grant CMMI 1031811. We would also like to thank Kevin Haas and Prashanth Selvaraj for their suggestions and insights.
Conflict of interests
The authors declare that they have no conflict of interest.
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