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Computational sleep scoring from multimodal neurophysiological time-series (polysomnography PSG) has achieved impressive clinical success. Models that use only a single electroencephalographic (EEG) channel from PSG have not yet received the same clinical recognition, since they lack Rapid Eye Movement (REM) scoring quality. The question whether this lack can be remedied at all remains an important one. We conjecture that predominant Long Short-Term Memory (LSTM) models do not adequately represent distant REM EEG segments (termed epochs), since LSTMs compress these to a fixed-size vector from separate past and future sequences. To this end, we introduce the EEG representation model ENGELBERT (electroEncephaloGraphic Epoch Local Bidirectional Encoder Representations from Transformer). It jointly attends to multiple EEG epochs from both past and future. Compared to typical token sequences in language, for which attention models have originally been conceived, overnight EEG sequences easily span more than 1000 30 s epochs. Local attention on overlapping windows reduces the critical quadratic computational complexity to linear, enabling versatile sub-one-hour to all-day scoring. ENGELBERT is at least one order of magnitude smaller than established LSTM models and is easy to train from scratch in a single phase. It surpassed state-of-the-art macro F1-scores in 3 single-EEG sleep scoring experiments. REM F1-scores were pushed to at least 86%. ENGELBERT virtually closed the gap to PSG-based methods from 4-5 percentage points (pp) to less than 1 pp F1-score., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Georg Dorffner is shareholder and part-time employee of The Siesta Group GmbH, a company providing services for clinical trials in the areas of EEG and sleep analysis., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Study Objectives: Manual scoring of polysomnograms is a time-consuming and tedious process. To expedite the scoring of polysomnograms, several computerized algorithms for automated scoring have been developed. The overarching goal of this study was to determine the validity of the Somnolyzer system, an automated system for scoring polysomnograms., Design: The analysis sample comprised of 97 sleep studies. Each polysomnogram was manually scored by certified technologists from four sleep laboratories and concurrently subjected to automated scoring by the Somnolyzer system. Agreement between manual and automated scoring was examined. Sleep staging and scoring of disordered breathing events was conducted using the 2007 American Academy of Sleep Medicine criteria., Setting: Clinical sleep laboratories., Measurements and Results: A high degree of agreement was noted between manual and automated scoring of the apnea-hypopnea index (AHI). The average correlation between the manually scored AHI across the four clinical sites was 0.92 (95% confidence interval: 0.90-0.93). Similarly, the average correlation between the manual and Somnolyzer-scored AHI values was 0.93 (95% confidence interval: 0.91-0.96). Thus, interscorer correlation between the manually scored results was no different than that derived from manual and automated scoring. Substantial concordance in the arousal index, total sleep time, and sleep efficiency between manual and automated scoring was also observed. In contrast, differences were noted between manually and automated scored percentages of sleep stages N1, N2, and N3., Conclusion: Automated analysis of polysomnograms using the Somnolyzer system provides results that are comparable to manual scoring for commonly used metrics in sleep medicine. Although differences exist between manual versus automated scoring for specific sleep stages, the level of agreement between manual and automated scoring is not significantly different than that between any two human scorers. In light of the burden associated with manual scoring, automated scoring platforms provide a viable complement of tools in the diagnostic armamentarium of sleep medicine., (© 2015 Associated Professional Sleep Societies, LLC.)

This chapter presents normative data on healthy sleep, as measured by polysomnography (PSG), from "supernormal" subjects across the age range from 20 to about 90 years. The data originates from the SIESTA project database established in the late 1990s. While that data has been published and used in research in many ways, the novelty of the current analysis is (a) the focus on normative data following the latest sleep staging standard (AASM 2012), and (b) the results after narrowing down the data set by excluding outliers due to disturbed sleep pattern that can occur in a sleep lab and are thus not examples of "normal" sleep. Results demonstrate interesting dependencies of sleep architecture on age, in particular a reduction in total sleep time and changes in sleep stage distributions toward lighter sleep, which differ in detail between the two genders.

Background: In 2007, the AASM Manual for the Scoring of Sleep and Associated Events was published by the American Academy of Sleep Medicine (AASM). Concerning the visual classification of sleep stages, these new rules are intended to replace the rules by Rechtschaffen and Kales (R&K)., Methods: We adapted the automatic R&K sleep scoring system Somnolyzer 24 × 7 to comply with the AASM rules and subsequently performed a validation study based on 72 polysomnographies from the Siesta database (56 healthy subjects, 16 patients, 38 females, 34 males, aged 21-86 years). Scorings according to the AASM rules were performed manually by experienced sleep scorers and semi-automatically by the AASM version of the Somnolyzer. Manual scorings and Somnolyzer reviews were performed independently by at least 2 out of 8 experts from 4 sleep centers., Results: In the quality control process, sleep experts corrected 4.8 and 3.7% of the automatically assigned epochs, resulting in a reliability between 2 Somnolyzer-assisted scorings of 99% (Cohen's kappa: 0.99). In contrast, the reliability between the 2 manual scorings was 82% (kappa: 0.76). The agreement between the 2 Somnolyzer-assisted and the 2 visual scorings was between 81% (kappa: 0.75) and 82% (kappa: 0.76)., Conclusion: The AASM version of the Somnolyzer revealed an agreement between semi-automated and human expert scoring comparable to that published for the R&K version with a validity comparable to that of human experts, but with a reliability close to 1, thereby reducing interrater variability as well as scoring time to a minimum., (Copyright © 2010 S. Karger AG, Basel.)

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 patients' 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 Cohen's kappa and Fleiss' kappa. The overall agreement was for the AASM standard 82.0% (Cohen's kappa = 0.76) and for the R&K standard 80.6% (Cohen's 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.

Objective: We developed a probabilistic continuous sleep stager based on Hidden Markov models using only a single EEG signal. It offers the advantage of being objective by not relying on human scorers, having much finer temporal resolution (1s instead of 30s), and being based on solid probabilistic principles rather than a predefined set of rules (Rechtschaffen & Kales), Methods and Material: Sixty-eight whole night sleep recordings from two different sleep labs are analysed using Gaussian observation Hidden Markov models., Results: Our unsupervised approach detects the cornerstones of human sleep (wakefulness, deep and REM sleep) with around 80% accuracy based on data from a single EEG channel. There are some difficulties in generalizing results across sleep labs., Conclusion: Using data from a single electrode is sufficient for reliable continuous sleep staging. Sleep recordings from different sleep labs are not directly comparable. Training of separate models for the sleep labs is necessary.

Abstract: We are introducing and validating an EEG data-based model of the sleep process with an arbitrary number of different sleep stages and a high time resolution allowing modeling of sleep microstructure. In contrast to the standard practice of sleep staging, defined by scoring rules, we describe sleep via posterior probabilities of a finite number of states, not necessarily reflecting the traditional sleep stages. To test the proposed probabilistic sleep model (PSM) for validity, we correlate statistics derived from the state posteriors with the results of psychometric tests, physiological variables and questionnaires collected before and after sleep. Considering short, in this study 3s long, data window the PSM allows describing the sleep process on finer time scale in comparison to the traditional sleep staging based on 20 or 30s long data segments visual inspection. By combining sleep states and using two measures derived from the posterior curves we show that the average absolute correlations between the measures and subjective and objective sleep quality measures are considerably higher when compared with the analogous measures derived from hypnograms based on sleep staging. In most cases these differences are significant. The results obtained with the PSM support its wider use in sleep process modeling research and these results also suggest that EEG signals contain more information about sleep than what sleep profiles based on discrete stages can reveal. Therefore the standardized scoring of sleep may not be sufficient to reveal important sleep changes related to subjective and objective sleep quality indexes. The proposed PSM represents a promising alternative. [Copyright &y& Elsevier]