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Extracting more information from EEG recordings for a better description of sleep.

Lewandowski A, Rosipal R, Dorffner G - Comput Methods Programs Biomed (2012)

Bottom Line: In most cases these differences are significant.The results obtained with the PSM support its wider use in sleep process modeling research and these results also suggest that EEG signals contain more information about sleep than what sleep profiles based on discrete stages can reveal.The proposed PSM represents a promising alternative.

View Article: PubMed Central - PubMed

Affiliation: Section for Artificial Intelligence, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Austria.

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Related in: MedlinePlus

Posteriors for two combinations of microstates. (a) Hypnogram. (b) Smoothed posterior for microstates 3, 11. (c) Smoothed posterior for microstates 2, 6, 7.
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fig0025: Posteriors for two combinations of microstates. (a) Hypnogram. (b) Smoothed posterior for microstates 3, 11. (c) Smoothed posterior for microstates 2, 6, 7.

Mentions: A very powerful property of the PSM is the fact that the posterior for a combination of microstates is the sum of posteriors. This feature allows defining new sleep states or sub-states by combining certain microstates. Using a larger number of 20 microstates allows partitioning the sleep space into fine details without losing the ability of re-combining the microstates according to different goals. These goals can be changed from application to application. For instance, the already described microstates 2, 3, 6, 7 and 11 from Table 2 are strongly related to S2. Two sets of these microstates can be defined (i) the spindle-rich S2R (combined states 2, 6 and 7) and (ii) S2F with few spindles (states 3 and 11). In Fig. 5 the posteriors of S2R and S2F are depicted. It can be observed that the posteriors of S2F have different heights for different S2 stages during the night. They seem to be higher if a SWS phase follows. Analogously, S2R seems to have its peaks predominantly at the beginning and end of the night.


Extracting more information from EEG recordings for a better description of sleep.

Lewandowski A, Rosipal R, Dorffner G - Comput Methods Programs Biomed (2012)

Posteriors for two combinations of microstates. (a) Hypnogram. (b) Smoothed posterior for microstates 3, 11. (c) Smoothed posterior for microstates 2, 6, 7.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4066998&req=5

fig0025: Posteriors for two combinations of microstates. (a) Hypnogram. (b) Smoothed posterior for microstates 3, 11. (c) Smoothed posterior for microstates 2, 6, 7.
Mentions: A very powerful property of the PSM is the fact that the posterior for a combination of microstates is the sum of posteriors. This feature allows defining new sleep states or sub-states by combining certain microstates. Using a larger number of 20 microstates allows partitioning the sleep space into fine details without losing the ability of re-combining the microstates according to different goals. These goals can be changed from application to application. For instance, the already described microstates 2, 3, 6, 7 and 11 from Table 2 are strongly related to S2. Two sets of these microstates can be defined (i) the spindle-rich S2R (combined states 2, 6 and 7) and (ii) S2F with few spindles (states 3 and 11). In Fig. 5 the posteriors of S2R and S2F are depicted. It can be observed that the posteriors of S2F have different heights for different S2 stages during the night. They seem to be higher if a SWS phase follows. Analogously, S2R seems to have its peaks predominantly at the beginning and end of the night.

Bottom Line: In most cases these differences are significant.The results obtained with the PSM support its wider use in sleep process modeling research and these results also suggest that EEG signals contain more information about sleep than what sleep profiles based on discrete stages can reveal.The proposed PSM represents a promising alternative.

View Article: PubMed Central - PubMed

Affiliation: Section for Artificial Intelligence, Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Austria.

Show MeSH
Related in: MedlinePlus