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Abstract Study objectives The wake-sleep transition zone represents a poorly defined borderland, containing, for example, microsleep episodes (MSEs), which are of potential relevance for diagnosis and may have consequences while driving. Yet, the scoring guidelines of the American Academy of Sleep Medicine (AASM) completely neglect it. We aimed to explore the borderland between wakefulness and sleep by developing the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring, focusing on MSEs visible in the electroencephalography (EEG), as opposed to purely behavior- or performance-defined MSEs. Methods Maintenance of Wakefulness Test (MWT) trials of 76 randomly selected patients were retrospectively scored according to both the AASM and the newly developed BERN scoring criteria. The visual scoring was compared with spectral analysis of the EEG. The quantitative EEG analysis enabled a reliable objectification of the visually scored MSEs. For less distinct episodes within the borderland, either ambiguous or no quantitative patterns were found. Results As expected, the latency to the first MSE was significantly shorter in comparison to the sleep latency, defined according to the AASM criteria. In certain cases, a large difference between the two latencies was observed and a substantial number of MSEs occurred between the first MSE and sleep. Series of MSEs were more frequent in patients with shorter sleep latencies, while isolated MSEs were more frequent in patients who did not reach sleep. Conclusion The BERN criteria extend the AASM criteria and represent a valuable tool for in-depth analysis of the wake-sleep transition zone, particularly important in the MWT.

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. 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. 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 ( = -0.54, < 0.05) and with the cumulative MSE duration in the driving simulator ( = 0.62, < 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. 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.

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.

In a cohort study among 2751 members (71.5% females) of the German and Swiss RLS patient organizations changes in restless legs syndrome (RLS) severity over time was assessed and the impact on quality of life, sleep quality and depressive symptoms was analysed. A standard set of scales (RLS severity scale IRLS, SF-36, Pittsburgh Sleep Quality Index and the Centre for Epidemiologic Studies Depression Scale) in mailed questionnaires was repeatedly used to assess RLS severity and health status over time and a 7-day diary once to assess short-term variations. A clinically relevant change of the RLS severity was defined by a change of at least 5 points on the IRLS scale. During 36 months follow-up minimal improvement of RLS severity between assessments was observed. Men consistently reported higher severity scores. RLS severity increased with age reaching a plateau in the age group 45-54 years. During 3 years 60.2% of the participants had no relevant (±5 points) change in RLS severity. RLS worsening was significantly related to an increase in depressive symptoms and a decrease in sleep quality and quality of life. The short-term variation showed distinctive circadian patterns with rhythm magnitudes strongly related to RLS severity. The majority of participants had a stable course of severe RLS over three years. An increase in RLS severity was accompanied by a small to moderate negative, a decrease by a small positive influence on quality of life, depressive symptoms and sleep quality.

Sleep-wake disturbances (SWD) are frequent in Parkinson's disease (PD). The effect of deep brain stimulation (DBS) on SWD is poorly known. In this study we examined the subjective and objective sleep-wake profile and the quality of life (QoL) of PD patients in the context of subthalamic DBS. We retrospectively analyzed data from PD patients and candidates for DBS in the nucleus suthalamicus (STN). Pre-DBS, sleep-wake assessments included subjective and objective (polysomnography, vigilance tests and actigraphy) measures. Post-DBS, subjective measures were collected. QoL was assessed using the Parkinson's Disease Questionnaire (PDQ-39) and the RAND SF-36-item Health Survey (RAND SF-36). Data from 74 PD patients (62% male, mean age 62.2 years, SD = 8.9) with a mean UPDRS-III (OFF) of 34.2 (SD = 14.8) and 11.8 (SD = 4.5) years under PD treatment were analyzed. Pre-DBS, daytime sleepiness, apathy, fatigue and depressive symptoms were present in 49%, 34%, 38% and 25% of patients respectively but not always as co-occurring symptoms. Sleep-wake disturbances were significantly correlated with QoL scores. One year after STN DBS, motor signs, QoL and sleepiness improved but apathy worsened. Changes in QoL were associated with changes in sleepiness and apathy but baseline sleep-wake functions were not predictive of STN DBS outcome. In PD patients presenting for STN DBS, subjective and objective sleep-wake disturbances are common and have a negative impact on QoL before and after neurosurgery. Given the current preliminary evidence, prospective observational studies assessing subjective and objective sleep-wake variables prior to and after DBS are needed.

Mehdi Tafti and colleagues identify new HLA class II haplotypes that are strongly protective against narcolepsy. Their analyses suggest a virtually causal role for the HLA region in determining narcolepsy susceptibility. Narcolepsy is a rare sleep disorder with the strongest human leukocyte antigen (HLA) association ever reported. Since the associated HLA - DRB1*1501-DQB1*0602 haplotype is common in the general population (15–25%), it has been suggested that it is almost necessary but not sufficient for developing narcolepsy. To further define the genetic basis of narcolepsy risk, we performed a genome-wide association study (GWAS) in 562 European individuals with narcolepsy (cases) and 702 ethnically matched controls, with independent replication in 370 cases and 495 controls, all heterozygous for DRB1*1501 - DQB1*0602 . We found association with a protective variant near HLA - DQA2 (rs2858884; P < 3 × 10 −8 ). Further analysis revealed that rs2858884 is strongly linked to DRB1*03-DQB1*02 ( P < 4 × 10 −43 ) and DRB1*1301-DQB1*0603 ( P < 3 × 10 −7 ). Cases almost never carried a trans DRB1*1301-DQB1*0603 haplotype (odds ratio = 0.02; P < 6 × 10 −14 ). This unexpected protective HLA haplotype suggests a virtually causal involvement of the HLA region in narcolepsy susceptibility.

An estimated 45 million persons in Europe are annually subjected to sleep-wake disorders. State-of-the-art polysomnography provides sophisticated insights into sleep (patho)physiology. A drawback of the method, however, is the obtrusive setting dependent on a clinical-based sleep laboratory with high operational costs. A contact-less prototype was developed to monitor limb movements and vital signs during sleep. A dual channel K-band Doppler radar transceiver captured limb movements and periodic chest wall motion due to respiration and heart activity. A wavelet transform based multi-resolution analysis (MRA) approach isolated limb movements, respiration, and heart rate from the demodulated signal. A test bench setup characterized the prototype simulating near physiological chest wall motions caused by periodic respiration and heartbeats in humans. Single- and multi-tone test bench simulations showed extremely low relative percentage errors of the prototype for respiratory and heart rate within −2 and 1%. The performance of the prototype was validated in overnight comparative studies, involving two healthy volunteers, with polysomnography as the reference. The prototype has successfully classified limb movements, with a sensitivity and specificity of 88.9 and 76.8% respectively, and has achieved accurate respiratory and heart rate measurement performance with overall absolute errors of 1 breath per minute for respiration and 3 beats per minute for heart rate. This pilot study shows that K-band Doppler radar and wavelet transform MRA seem to be valid for overnight sleep marker assessment. The contact-less approach might offer a promising solution for home-based sleep monitoring and assessment.

Narcolepsy is a chronic sleep disorder caused by the loss of neurons that produce hypocretin. The close association with HLA-DQB1*06:02 , evidence for immune dysregulation and increased incidence upon influenza vaccination together suggest that this disorder has an autoimmune origin. However, there is little evidence of autoreactive lymphocytes in patients with narcolepsy. Here we used sensitive cellular screens and detected hypocretin-specific CD4 + T cells in all 19 patients that we tested; T cells specific for tribbles homologue 2—another self-antigen of hypocretin neurons—were found in 8 out of 13 patients. Autoreactive CD4 + T cells were polyclonal, targeted multiple epitopes, were restricted primarily by HLA-DR and did not cross-react with influenza antigens. Hypocretin-specific CD8 + T cells were also detected in the blood and cerebrospinal fluid of several patients with narcolepsy. Autoreactive clonotypes were serially detected in the blood of the same—and even of different—patients, but not in healthy control individuals. These findings solidify the autoimmune aetiology of narcolepsy and provide a basis for rapid diagnosis and treatment of this disease. The detection of hypocretin-specific autoreactive CD4 + and CD8 + T cells in patients with narcolepsy reveals the autoimmune aetiology of this disorder.

Abstract Study Objectives Microsleep episodes (MSEs) are brief episodes of sleep, mostly defined to be shorter than 15 s. In the electroencephalogram (EEG), MSEs are mainly characterized by a slowing in frequency. The identification of early signs of sleepiness and sleep (e.g. MSEs) is of considerable clinical and practical relevance. Under laboratory conditions, the maintenance of wakefulness test (MWT) is often used for assessing vigilance. Methods We analyzed MWT recordings of 76 patients referred to the Sleep-Wake-Epilepsy-Center. MSEs were scored by experts defined by the occurrence of theta dominance on ≥1 occipital derivation lasting 1–15 s, whereas the eyes were at least 80% closed. We calculated spectrograms using an autoregressive model of order 16 of 1 s epochs moved in 200 ms steps in order to visualize oscillatory activity and derived seven features per derivation: power in delta, theta, alpha and beta bands, ratio theta/(alpha + beta), quantified eye movements, and median frequency. Three algorithms were used for MSE classification: support vector machine (SVM), random forest (RF), and an artificial neural network (long short-term memory [LSTM] network). Data of 53 patients were used for the training of the classifiers, and 23 for testing. Results MSEs were identified with a high performance (sensitivity, specificity, precision, accuracy, and Cohen’s kappa coefficient). Training revealed that delta power and the ratio theta/(alpha + beta) were most relevant features for the RF classifier and eye movements for the LSTM network. Conclusions The automatic detection of MSEs was successful for our EEG-based definition of MSEs, with good performance of all algorithms applied.