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Annelies Santschi,1,* David R Schreier,1,* Anneke Hertig-Godeschalk,1 Samuel EJ Knobel,1 Uli S Herrmann,1 Jelena Skorucak,2 Wolfgang J Schmitt,3 Johannes Mathis1,4 1Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; 2University Children’s Hospital Zurich, Zurich, Switzerland; 3University Hospital of Psychiatry, University of Bern, Bern, Switzerland; 4Sleep Medicine, Neurozentrum Bern, Bern, Switzerland*These authors contributed equally to this workCorrespondence: David R Schreier, Department of Neurology, Inselspital, Bern University Hospital, Freiburgstrasse 16, Bern, CH-3010, Switzerland, Tel +41 31 632 21 11, Email david.r.schreier@gmail.comPurpose: Differential diagnosis of central disorders of hypersomnolence remains challenging, particularly between idiopathic (IH) and nonorganic hypersomnia (NOH). We hypothesized that eyelid closure behavior in the maintenance of wakefulness test (MWT) could be a valuable biomarker.Patients and Methods: MWT recordings of patients with IH, NOH, narcolepsy-cataplexy (NC), and healthy sleep-deprived controls (H) were retrospectively analyzed (15 individuals per group). For each MWT trial, visual scoring of face videography for partial (50– 80%) and full eyelid closure (≥ 80%) was performed from “lights off” to the first microsleep episode (≥ 3 s).Results: In all groups, the frequency and cumulative duration of periods with partial and full eyelid closure gradually increased toward the first microsleep episode. On the group level, significant differences occurred for the latency to the first microsleep episode (IH 21 min (18– 33), NOH 23 min (17– 35), NC 11 min (7– 19), H 10 min (6– 25); p = 0.009), the ratio between partial and full eyelid closure duration (IH 2.2 (0.9– 3.1), NOH 0.5 (0– 1.2), NC 2.8 (1.1– 5), H 0.7 (0.4– 3.3); p = 0.004), and the difference between full and partial eyelid closure duration in the five minutes prior to the first microsleep episode (∆full – partial eyelid closure duration: IH − 16 s (− 35 to 28); NOH 46 s (9– 82); NC − 6 s (− 26 to 5); H 10 s (− 4 to 18); p = 0.007). IH and NOH significantly differed comparing the ratio between partial and full eyelid closure (p = 0.005) and the difference between ∆full – partial eyelid closure duration in the five minutes prior to the first microsleep episode (p = 0.006).Conclusion: In the MWT, eyelid closure behavior (∆full – partial) in the period prior to the first microsleep episode could be of value for discriminating NOH from other etiologies of excessive daytime sleepiness, particularly IH.Keywords: hypersomnia, hypersomnia associated with psychiatric disorders, excessive daytime sleepiness, vigilance test, central disorders of hypersomnolence, microsleep

Background: Deep brain stimulation of the central thalamus (CT-DBS) has potential for modulating states of consciousness, but it can also trigger electrographic seizures, including poly-spike-wave trains (PSWT)., Objectives: To report the probability of inducing PSWTs during CT-DBS in awake, freely-moving mice., Methods: Mice were implanted with electrodes to deliver unilateral and bilateral CT-DBS at different frequencies while recording electroencephalogram (EEG). We titrated stimulation current by gradually increasing it at each frequency until a PSWT appeared. Subsequent stimulations to test arousal modulation were performed at the current one step below the current that caused a PSWT during titration., Results: In 2.21% of the test stimulations (10 out of 12 mice), CT-DBS caused PSWTs at currents lower than the titrated current, including currents as low as 20 μA., Conclusion: Our study found a small but significant probability of inducing PSWTs even after titration and at relatively low currents. EEG should be closely monitored for electrographic seizures when performing CT-DBS in both research and clinical settings., Competing Interests: Declaration of competing interest Emery N. Brown holds patents on anesthetic state monitoring and control; holds founding interest in PASCALL, a start-up developing physiological monitoring systems; receives royalties from intellectual property through Massachusetts General Hospital licensed to Masimo. The interests of Emery N. Brown were reviewed and are managed by Massachusetts General Hospital and Mass General Brigham in accordance with their conflict of interest policies. The rest of the authors do not have any interest to report., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30 s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We performed a visualization of the internal representation of the data by the artificial neuronal network performing best using t-distributed stochastic neighbor embedding (t-SNE). Visualization revealed that MSEs and wakefulness were mostly separable, though not entirely, and MSEc and ED largely intersected with the two main classes. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. The code of the algorithms (https://github.com/alexander-malafeev/microsleep-detection) and data (https://zenodo.org/record/3251716) are available., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Malafeev, Hertig-Godeschalk, Schreier, Skorucak, Mathis and Achermann.)

Study Objectives: Microsleep episodes (MSEs) are short fragments of sleep (1-15 s) that can cause dangerous situations with potentially fatal outcomes. In the diagnostic sleep-wake and fitness-to-drive assessment, accurate and early identification of sleepiness is essential. However, in the absence of a standardised definition and a time-efficient scoring method of MSEs, these short fragments are not assessed in clinical routine. Based on data of moderately sleepy patients, we recently developed the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring of MSEs and corresponding machine learning algorithms for automatic MSE detection, both mainly based on the electroencephalogram (EEG). The present study aimed to investigate the relationship between automatically detected MSEs and driving performance in a driving simulator, recorded in parallel with EEG, and to assess algorithm performance for MSE detection in severely sleepy participants. Methods: Maintenance of wakefulness test (MWT) and driving simulator recordings of 18 healthy participants, before and after a full night of sleep deprivation, were retrospectively analysed. Performance of automatic detection was compared with visual MSE scoring, following the BERN criteria, in MWT recordings of 10 participants. Driving performance was measured by the standard deviation of lateral position and the occurrence of off-road events. Results: In comparison to visual scoring, automatic detection of MSEs in participants with severe sleepiness showed good performance (Cohen's kappa = 0.66). The MSE rate in the MWT correlated with the latency to the first MSE in the driving simulator ( r s = -0.54, p < 0.05) and with the cumulative MSE duration in the driving simulator ( r s = 0.62, p < 0.01). No correlations between MSE measures in the MWT and driving performance measures were found. In the driving simulator, multiple correlations between MSEs and driving performance variables were observed. Conclusion: Automatic MSE detection worked well, independent of the degree of sleepiness. The rate and the cumulative duration of MSEs could be promising sleepiness measures in both the MWT and the driving simulator. The correlations between MSEs in the driving simulator and driving performance might reflect a close and time-critical relationship between sleepiness and performance, potentially valuable for the fitness-to-drive assessment., (Copyright © 2020 Skorucak, Hertig-Godeschalk, Achermann, Mathis and Schreier.)