Temporal evolution of coherence and power in the human sleep electroencephalogram

Achermann, Peter; Borbély, Alexander A.

doi:10.1046/j.1365-2869.7.s1.6.x

Cited by 81 publications

(41 citation statements)

References 24 publications

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“…Interestingly, delta coherence during the transition was very similar to that observed during QW ( p ϭ 0.046; paired Student's t test) and significantly lower than the average of QW and SWS values, ( p ϭ 0.012; paired Student's t test), whereas no significant difference was found at spindle range ( p ϭ 0.201; paired Student's t test). Altogether, these results indicate that delta coherence changes were slower and less prominent than changes in spindle coherence, supporting the notion that transitions into SWS mainly involve changes in the magnitude of spindle coherence (Achermann and Borbely, 1998b).…”

Section: Forebrain Dynamics During State Transitionssupporting

confidence: 77%

Global Forebrain Dynamics Predict Rat Behavioral States and Their Transitions

Gervasoni¹,

Lin²,

Ribeiro³

et al. 2004

J. Neurosci.

295

336

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The wake-sleep cycle, a spontaneous succession of global brain states that correspond to major overt behaviors, occurs in all higher vertebrates. The transitions between these states, at once rapid and drastic, remain poorly understood. Here, intracranial local field potentials (LFPs) recorded in the cortex, hippocampus, striatum, and thalamus were used to characterize the neurophysiological correlates of the rat wake-sleep cycle. By way of a new method for the objective classification and quantitative investigation of all major brain states, we demonstrate that global brain state transitions occur simultaneously across multiple forebrain areas as specific spectral trajectories with characteristic path, duration, and coherence bandwidth. During state transitions, striking changes in neural synchronization are effected by the prominent narrow-band LFP oscillations that mark state boundaries. Our results demonstrate that distant forebrain areas tightly coordinate the processing of neural information during and between global brain states, indicating a very high degree of functional integration across the entire wake-sleep cycle. We propose that transient oscillatory synchronization of synaptic inputs, which underlie the rapid switching of global brain states, may facilitate the exchange of information within and across brain areas at the boundaries of very distinct neural processing regimens.

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Section: Forebrain Dynamics During State Transitionssupporting

confidence: 77%

Global Forebrain Dynamics Predict Rat Behavioral States and Their Transitions

Gervasoni¹,

Lin²,

Ribeiro³

et al. 2004

J. Neurosci.

295

336

View full text Add to dashboard Cite

show abstract

“…Though the automatic analysis of sleep EEG recordings by means of mathematical approaches, such as spectral and coherence analysis [40] , period-amplitude analysis [41] , autoregressive modeling [42] or Hidden-Markov models [43][44][45] , has come to play an important role in scientifi c research, in clinical routine the only standard that has found worldwide acceptance is the visual classifi cation of sleep stages according to R&K. Even in scientifi c sleep studies, R&K results are usually presented as well -at least to document sleep quality and sleep architecture of the population studied. Thus, no matter what a sleep study aims at, all data are evaluated according to R&K criteria based on either visual scorings or semi-automatic evaluations with subsequent subjectively and time-consuming visual editing (see also recommendations of national and international sleep associations and societies, e.g.…”

Section: Discussionmentioning

confidence: 99%

An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database

Anderer

Gruber²,

Parapatics³

et al. 2005

Neuropsychobiology

260

211

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To date, the only standard for the classification of sleep-EEG recordings that has found worldwide acceptance are the rules published in 1968 by Rechtschaffen and Kales. Even though several attempts have been made to automate the classification process, so far no method has been published that has proven its validity in a study including a sufficiently large number of controls and patients of all adult age ranges. The present paper describes the development and optimization of an automatic classification system that is based on one central EEG channel, two EOG channels and one chin EMG channel. It adheres to the decision rules for visual scoring as closely as possible and includes a structured quality control procedure by a human expert. The final system (Somnolyzer 24 × 7™) consists of a raw data quality check, a feature extraction algorithm (density and intensity of sleep/wake-related patterns such as sleep spindles, delta waves, SEMs and REMs), a feature matrix plausibility check, a classifier designed as an expert system, a rule-based smoothing procedure for the start and the end of stages REM, and finally a statistical comparison to age- and sex-matched normal healthy controls (Siesta Spot Report™). The expert system considers different prior probabilities of stage changes depending on the preceding sleep stage, the occurrence of a movement arousal and the position of the epoch within the NREM/REM sleep cycles. Moreover, results obtained with and without using the chin EMG signal are combined. The Siesta polysomnographic database (590 recordings in both normal healthy subjects aged 20–95 years and patients suffering from organic or nonorganic sleep disorders) was split into two halves, which were randomly assigned to a training and a validation set, respectively. The final validation revealed an overall epoch-by-epoch agreement of 80% (Cohen’s kappa: 0.72) between the Somnolyzer 24 × 7 and the human expert scoring, as compared with an inter-rater reliability of 77% (Cohen’s kappa: 0.68) between two human experts scoring the same dataset. Two Somnolyzer 24 × 7 analyses (including a structured quality control by two human experts) revealed an inter-rater reliability close to 1 (Cohen’s kappa: 0.991), which confirmed that the variability induced by the quality control procedure, whereby approximately 1% of the epochs (in 9.5% of the recordings) are changed, can definitely be neglected. Thus, the validation study proved the high reliability and validity of the Somnolyzer 24 × 7 and demonstrated its applicability in clinical routine and sleep studies.

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“…Coherence may be more sensitive than amplitude to distinguish TM sessions from other conditions. Other authors have also reported a dissociation between alpha power and coherence during TM practice (Dillbeck & Bronson, 1981), during cognitive tasks (Petsche, Kaplan, von Stein, & Filz, 1997), during normal aging (Koyama et al, 1997), and during sleep (Achermann & Borbely, 1998).…”

Section: Consideration Of Nonsignificant Findingsmentioning

confidence: 90%