Functional significance of stage 2 sleep spindle activity for declarative memory consolidation. Randomized, within-subject, multicenter. Weekly sleep laboratory visits, actigraphy, and sleep diary (4 weeks). Twenty-four healthy subjects (12 men) aged between 20 and 30 years. Declarative memory task or nonlearning control task before sleep. This study measured spindle activity during stage 2 sleep following a (declarative) word-pair association task as compared to a control task. Participants performed a cued recall in the evening after learning (160 word pairs) as well as in the subsequent morning after 8 hours of undisturbed sleep with full polysomnography. Overnight change in the number of recalled words, but not absolute memory performance, correlated significantly with increased spindle activity during the experimental night (r₂₄ = .63, P < .01). Time spent in each sleep stage could not account for this relationship. 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.

To investigate differences between visual sleep scoring according to the classification developed by Rechtschaffen and Kales (R&K, 1968) and scoring based on the new guidelines of the American Academy of Sleep Medicine (AASM, 2007). All-night polysomnographic recordings were scored visually according to the R&K and AASM rules by experienced sleep scorers. Descriptive data analysis was used to compare the resulting sleep parameters. Healthy subjects and patients (38 females and 34 males) aged between 21 and 86 years. N/A While sleep latency and REM latency, total sleep time, and sleep efficiency were not affected by the classification standard, the time (in minutes and in percent of total sleep time) spent in sleep stage 1 (S1/N1), stage 2 (S2/N2) and slow wave sleep (S3+S4/N3) differed significantly between the R&K and the AASM classification. While light and deep sleep increased (S1 vs. N1 [+10.6 min, (+2.8%)]: P < 0.01; S3+S4 vs. N3 [+9.1 min (+2.4%)]: P < 0.01), stage 2 sleep decreased significantly according to AASM rules (S2 vs. N2 [–20.5 min, (–4.9%)]: P < 0.01). Moreover, wake after sleep onset was significantly prolonged by approximately 4 minutes (P < 0.01) according to the AASM standard. Interestingly, the effects on stage REM were age-dependent (intercept at 20 years: –7.5 min; slope: 1.6 min for 10- year age increase). No effects of sex and diagnosis were observed. The study shows significant and age-dependent differences between sleep parameters derived from conventional visual sleep scorings on the basis of R&K rules and those based on the new AASM rules. Thus, new normative data have to be established for the AASM standard.

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.

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.

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.