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.

SUMMARY Interrater variability of sleep stage scorings has an essential impact not only on the reading of polysomnographic sleep studies (PSGs) for clinical trials but also on the evaluation of patientsÕ sleep. With the introduction of a new standard for sleep stage scorings (AASM standard) there is a need for studies on interrater reliability (IRR). The SIESTA database resulting from an EU-funded project provides a large number of studies (n = 72; 56 healthy controls and 16 subjects with different sleep disorders, mean age ± SD: 57.7 ± 18.7, 34 females) for which scorings according to both standards (AASM and R&K) were done. Differences in IRR were analysed at two levels: (1) based on quantitative sleep parameter by means of intraclass correlations; and (2) based on an epoch-by-epoch comparison by means of CohenÕs kappa and FleissÕ kappa. The overall agreement was for the AASM standard 82.0% (CohenÕs kappa = 0.76) and for the R&K standard 80.6% (CohenÕs kappa = 0.68). Agreements increased from R&K to AASM for all sleep stages, except N2. The results of this study underline that the modification of the scoring rules improve IRR as a result of the integration of occipital, central and frontal leads on the one hand, but decline IRR on the other hand specifically for N2, due to the new rule that cortical arousals with or without concurrent increase in submental electromyogram are critical events for the end of N2.k e y w o r d s AASM scoring standard, interrater reliability, Rechtschaffen and Kales, SIESTA project, sleep stage scoring

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.

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.