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Erschienen in: Journal of Computational Neuroscience 1/2014

01.08.2014

A probabilistic framework for a physiological representation of dynamically evolving sleep state

verfasst von: Vera M. Dadok, Heidi E. Kirsch, Jamie W. Sleigh, Beth A. Lopour, Andrew J. Szeri

Erschienen in: Journal of Computational Neuroscience | Ausgabe 1/2014

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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.

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Fußnoten
1
In previous work a measure other than \(\tilde {h}_{e}\) was used for feedback control to reflect more accurately an EEG measurement (Lopour and Szeri 2010), however for the purposes of this analysis, which does not require electrical input based on precise measurements, \(\tilde {h}_{e}\) will serve well as it does in Lopour et al. (2011).
 
2
This transition matrix and prior assumption can be relaxed or altered without a significant change to the algorithm. For instance, a transition matrix may be learned using the Baum-Welch algorithm given sufficient data and under certain assumptions.
 
3
Online Resource 1 shows Subject 3 data mapped onto the parameter plane using the methods described above. The likelihood is represented with the underlying colormap, and a convex hull containing 80 % of the likelihood is superimposed, the color of which indicates the epoch sleep stage. The movie title indicates the epoch count from sleep onset, which occurred at epoch 1196 from the start of the file.
 
4
This method is a real-time method in the context of each 30-second epoch constituting an instant of time. However note that minor changes would need to be incorporated into the implementation of this method to achieve effective real time evaluation–primarily, the RMS normalization of the human subject data would need to be calculated using a real-time approximation of the normalization parameters (an approximation of the RMS). We would not expect this approximation to change significantly the results.
 
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Metadaten
Titel
A probabilistic framework for a physiological representation of dynamically evolving sleep state
verfasst von
Vera M. Dadok
Heidi E. Kirsch
Jamie W. Sleigh
Beth A. Lopour
Andrew J. Szeri
Publikationsdatum
01.08.2014
Verlag
Springer US
Erschienen in
Journal of Computational Neuroscience / Ausgabe 1/2014
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI
https://doi.org/10.1007/s10827-013-0489-x

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