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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]