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Analysis of the temporal organization of sleep spindles in the human sleep EEG using a phenomenological modeling approach.

Olbrich E, Achermann P - J Biol Phys (2008)

Bottom Line: Recently, we proposed a method to detect and analyze these patterns using linear autoregressive models for short (approximately 1 s) data segments.We analyzed the temporal organization of sleep spindles and discuss to what extent the observed interevent intervals correspond to properties of stationary stochastic processes and whether additional slow processes, such as slow oscillations, have to be assumed.We have found evidence for such an additional slow process, most pronounced in sleep stage 2.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. olbrich@mis.mpg.de

ABSTRACT
The sleep electroencephalogram (EEG) is characterized by typical oscillatory patterns such as sleep spindles and slow waves. Recently, we proposed a method to detect and analyze these patterns using linear autoregressive models for short (approximately 1 s) data segments. We analyzed the temporal organization of sleep spindles and discuss to what extent the observed interevent intervals correspond to properties of stationary stochastic processes and whether additional slow processes, such as slow oscillations, have to be assumed. We have found evidence for such an additional slow process, most pronounced in sleep stage 2.

No MeSH data available.


Normalized histograms (pooled data) of interevent intervals (Δt) in the spindle frequency range (11.5–16 Hz) in nonrapid eye movement sleep stage 2 (ST2; top left) and slow wave sleep (SWS, stages 3 and 4; top right) and of events in the spindle frequency range detected from data generated by stationary AR(4) models with r = 0.94 (bottom left) and r = 0.89 (bottom right) for the spindle frequency
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Fig4: Normalized histograms (pooled data) of interevent intervals (Δt) in the spindle frequency range (11.5–16 Hz) in nonrapid eye movement sleep stage 2 (ST2; top left) and slow wave sleep (SWS, stages 3 and 4; top right) and of events in the spindle frequency range detected from data generated by stationary AR(4) models with r = 0.94 (bottom left) and r = 0.89 (bottom right) for the spindle frequency

Mentions: However, there are also inconsistencies in relation to this hypothesis: First, the frequency of the slow oscillations was originally considered as being only slightly < 1 Hz. Achermann and Borbély [12], for instance, reported a peak in the power spectrum at 0.7 Hz. This would correspond to a shorter typical interevent interval between sleep spindles than 4 s. Thus, only approximately every third slow oscillation would trigger a spindle. Second, the rhythmic occurrence of spindles is most pronounced in sleep stage 2 (see Fig. 4), but slow oscillations are most prominent in deep sleep. Thus, one should also take alternative explanations into account.


Analysis of the temporal organization of sleep spindles in the human sleep EEG using a phenomenological modeling approach.

Olbrich E, Achermann P - J Biol Phys (2008)

Normalized histograms (pooled data) of interevent intervals (Δt) in the spindle frequency range (11.5–16 Hz) in nonrapid eye movement sleep stage 2 (ST2; top left) and slow wave sleep (SWS, stages 3 and 4; top right) and of events in the spindle frequency range detected from data generated by stationary AR(4) models with r = 0.94 (bottom left) and r = 0.89 (bottom right) for the spindle frequency
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2585623&req=5

Fig4: Normalized histograms (pooled data) of interevent intervals (Δt) in the spindle frequency range (11.5–16 Hz) in nonrapid eye movement sleep stage 2 (ST2; top left) and slow wave sleep (SWS, stages 3 and 4; top right) and of events in the spindle frequency range detected from data generated by stationary AR(4) models with r = 0.94 (bottom left) and r = 0.89 (bottom right) for the spindle frequency
Mentions: However, there are also inconsistencies in relation to this hypothesis: First, the frequency of the slow oscillations was originally considered as being only slightly < 1 Hz. Achermann and Borbély [12], for instance, reported a peak in the power spectrum at 0.7 Hz. This would correspond to a shorter typical interevent interval between sleep spindles than 4 s. Thus, only approximately every third slow oscillation would trigger a spindle. Second, the rhythmic occurrence of spindles is most pronounced in sleep stage 2 (see Fig. 4), but slow oscillations are most prominent in deep sleep. Thus, one should also take alternative explanations into account.

Bottom Line: Recently, we proposed a method to detect and analyze these patterns using linear autoregressive models for short (approximately 1 s) data segments.We analyzed the temporal organization of sleep spindles and discuss to what extent the observed interevent intervals correspond to properties of stationary stochastic processes and whether additional slow processes, such as slow oscillations, have to be assumed.We have found evidence for such an additional slow process, most pronounced in sleep stage 2.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. olbrich@mis.mpg.de

ABSTRACT
The sleep electroencephalogram (EEG) is characterized by typical oscillatory patterns such as sleep spindles and slow waves. Recently, we proposed a method to detect and analyze these patterns using linear autoregressive models for short (approximately 1 s) data segments. We analyzed the temporal organization of sleep spindles and discuss to what extent the observed interevent intervals correspond to properties of stationary stochastic processes and whether additional slow processes, such as slow oscillations, have to be assumed. We have found evidence for such an additional slow process, most pronounced in sleep stage 2.

No MeSH data available.