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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 (electro En cephalo G raphic E poch L ocal B idirectional E ncoder R epresentations from T ransformer). 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. [ABSTRACT FROM AUTHOR]

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]

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 (1 s instead of 30 s), 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. [Copyright &y& Elsevier]

Abstract: One of the standard applications of Independent Component Analysis (ICA) to EEG is removal of artifacts due to movements of the eye bulbs. Short blinks as well as slower saccadic movements are removed by subtracting respective independent components (ICs). EEG recorded from blind subjects poses special problems, since it shows a higher quantity of eye movements, which are also more irregular and very different across subjects. It is demonstrated that ICA can still be of use by comparing results from four blind subjects with results from one subject without eye bulbs who therefore does not show eye movement artifacts at all. [Copyright &y& Elsevier]

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. Copyright © 2005 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]