A growing body of evidence supports the active role of sleep for information reprocessing. Whereas past research focused mainly on the distinct rapid eye movement and slow-wave sleep, these results indicate that increased sleep stage 2 spindle activity is related to an increase in recall performance and, thus, may reflect memory consolidation.

Our study clearly demonstrates that depressive mood is the main factor influencing QOL. The disability status, fatigue and reduced sleep quality have an impact mainly on physical domains of life quality.

Stage 2 sleep spindles have been previously viewed as useful markers for the development and integrity of the CNS and were more currently linked to 'offline re-processing' of implicit as well as explicit memory traces. Additionally, it had been discussed if spindles might be related to a more general learning or cognitive ability. In the present multicentre study we examined the relationship of automatically detected slow (< 13 Hz) and fast (> 13 Hz) stage 2 sleep spindles with: (i) the Raven's Advanced Progressive Matrices (testing 'general cognitive ability'); as well as (ii) the Wechsler Memory scale-revised (evaluating memory in various subdomains). Forty-eight healthy subjects slept three times (separated by 1 week) for a whole night in a sleep laboratory with complete polysomnographic montage. Whereas the first night only served adaptation and screening purposes, the two remaining nights were preceded either by an implicit mirror-tracing or an explicit word-pair association learning or (corresponding) control task. Robust relationships of slow and fast sleep spindles with both cognitive as well as memory abilities were found irrespectively of whether learning occurred before sleep. Based on the present findings we suggest that besides being involved in shaping neuronal networks after learning, sleep spindles do reflect important aspects of efficient cortical-subcortical connectivity, and are thereby linked to cognitive- and memory-related abilities alike.

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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SUMMAR Y Interrater variability of sleep stage scorings is a well-known phenomenon. The SIESTA project offered the opportunity to analyse interrater reliability (IRR) between experienced scorers from eight European sleep laboratories within a large sample of patients with different (sleep) disorders: depression, general anxiety disorder with and without non-organic insomnia, Parkinson's disease, period limb movements in sleep and sleep apnoea. The results were based on 196 recordings from 98 patients (73 males: 52.3 ± 12.1 years and 25 females: 49.5 ± 11.9 years) for which two independent expert scorings from two different laboratories were available. Cohen's j was used to evaluate the IRR on the basis of epochs and intraclass correlation was used to analyse the agreement on quantitative sleep parameters. The overall level of agreement when five different stages were distinguished was j ¼ 0.6816 (76.8%), which in terms of j reflects a 'substantial' agreement (Landis and Koch, 1977). For different groups of patients j values varied from 0.6138 (Parkinson's disease) to 0.8176 (generalized anxiety disorder). With regard to (sleep) stages, the IRR was highest for rapid eye movement (REM), followed by Wake, slow-wave sleep (SWS), non-rapid eye movement 2 (NREM2) and NREM1. The results of regression analysis showed that age and sex only had a statistically significant effect on j when the (sleep) stages are considered separately. For NREM2 and SWS a statistically significant decrease of IRR with age has been observed and the IRR for SWS was lower for males than for females. These variations of IRR most probably reflect changes of the sleep electroencephalography (EEG) with age and gender.

Dopaminergic agents such as ropinirole are the drugs of first choice in treating restless legs syndrome (RLS). Recently, gabapentin, a structural analogue of γ-aminobutyric acid, has also been shown to improve sensorimotor symptoms in RLS. Therefore, the tolerability and efficacy of randomized treatment with either gabapentin or ropinirole in patients with idiopathic RLS was evaluated in this 4-week open clinical trial. Patients with idiopathic RLS were treated with either 300 mg of gabapentin (n = 8) or 0.5 mg of ropinirole (n = 8) as the initial dose, and the dose was up-titrated until relief of symptoms was achieved (gabapentin mean dosage 800 ± 397 mg, range 300–1,200 mg; ropinirole mean dosage 0.78 ± 0.47 mg, range 0.25–1.50 mg). In both groups, International Restless Legs Syndrome Study Group questionnaire scores improved significantly (p ≤ 0.018), whereas the scores of the Epworth sleepiness scale remained unchanged within normal limits. Polysomnographic data showed a reduction of periodic leg movements during sleep (PLMS; p < 0.03) and PLMS index (p < 0.02) in both groups. Side effects were only mild and mostly transient. After 6–10 months of follow-up, in most patients, RLS symptoms were still improved. We conclude that gabapentin and ropinirole provide a similarly well-tolerated and effective treatment of PLMS and sensorimotor symptoms in patients with idiopathic RLS.

Quantitative analysis of sleep EEG data can provide valuable additional information in sleep research. However, analysis of data contaminated by artifacts can lead to spurious results. Thus, the first step in realizing an automatic sleep analysis system is the implementation of a reliable and valid artifact processing strategy. This strategy should include: (1) high-quality recording techniques in order to minimize the occurrence of avoidable artifacts (e.g. technical artifacts); (2) artifact minimization procedures in order to minimize the loss of data by estimating the contribution of different artifacts in the EEG recordings, thus allowing the calculation of the ‘corrected’ EEG (e.g. ocular and ECG interference), and finally (3) artifact identification procedures in order to define epochs contaminated by remaining artifacts (e.g. movement and muscle artifacts). Therefore, after a short description of the types of artifacts in the sleep EEG and some typical examples obtained in different sleep stages, artifact minimization and identification procedures will be reviewed.