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Computational and Mathematical Methods in Medicine
Volume 2016 (2016), Article ID 6450126, 8 pages
Research Article

Generalized Information Equilibrium Approaches to EEG Sleep Stage Discrimination

1Department of Psychiatry, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA 90073, USA
2Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, CA, USA
3The Boeing Company, Seattle, WA 98124, USA

Received 15 March 2016; Revised 28 May 2016; Accepted 19 June 2016

Academic Editor: Valeri Makarov

Copyright © 2016 Todd Zorick and Jason Smith. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recent advances in neuroscience have raised the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG) signals is via power-law distributed neuronal avalanches, while EEG signals are nonstationary. Therefore, spectral analysis of EEG may miss many properties inherent in such signals. A complete understanding of such dynamical systems requires knowledge of the underlying nonequilibrium thermodynamics. In recent work by Fielitz and Borchardt (2011, 2014), the concept of information equilibrium (IE) in information transfer processes has successfully characterized many different systems far from thermodynamic equilibrium. We utilized a publicly available database of polysomnogram EEG data from fourteen subjects with eight different one-minute tracings of sleep stage 2 and waking and an overlapping set of eleven subjects with eight different one-minute tracings of sleep stage 3. We applied principles of IE to model EEG as a system that transfers (equilibrates) information from the time domain to scalp-recorded voltages. We find that waking consciousness is readily distinguished from sleep stages 2 and 3 by several differences in mean information transfer constants. Principles of IE applied to EEG may therefore prove to be useful in the study of changes in brain function more generally.