Eyelid Closure Behavior of Patients with Idiopathic and Nonorganic Hypersomnia, Narcolepsy-Cataplexy, and Healthy Controls in the Maintenance of Wakefulness Test.

Purpose: 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.

Automated analysis of a large-scale paediatric dataset illustrates the interdependent relationship between epilepsy and sleep.

Slow waves are an electrophysiological characteristic of non-rapid eye movement sleep and a marker of the restorative function of sleep. In certain pathological conditions, such as different types of epilepsy, slow-wave sleep is affected by epileptiform discharges forming so-called "spike-waves". Previous evidence shows that the overnight change in slope of slow waves during sleep is impaired under these conditions. However, these past studies were performed in a small number of patients, considering only short segments of the recording night. Here, we screened a clinical data set of 39'179 pediatric EEG recordings acquired in the past 25 years (1994-2019) at the University Children's Hospital Zurich and identified 413 recordings of interest. We applied an automated approach based on machine learning to investigate the relationship between sleep and epileptic spikes in this large-scale data set. Our findings show that the overnight change in the slope of slow waves was correlated with the spike-wave index, indicating that the impairment of the net reduction in synaptic strength during sleep is spike dependent.

Boosting Recovery During Sleep by Means of Auditory Stimulation.

Sufficient recovery during sleep is the basis of physical and psychological well-being. Understanding the physiological mechanisms underlying this restorative function is essential for developing novel approaches to promote recovery during sleep. Phase-targeted auditory stimulation (PTAS) is an increasingly popular technique for boosting the key electrophysiological marker of recovery during sleep, slow-wave activity (SWA, 1-4 Hz EEG power). However, it is unknown whether PTAS induces physiological sleep. In this study, we demonstrate that, when applied during deep sleep, PTAS accelerates SWA decline across the night which is associated with an overnight improvement in attentional performance. Thus, we provide evidence that PTAS enhances physiological sleep and demonstrate under which conditions this occurs most efficiently. These findings will be important for future translation into clinical populations suffering from insufficient recovery during sleep.

Neuromodulation by means of phase-locked auditory stimulation affects key marker of excitability and connectivity during sleep.

The propagating pattern of sleep slow waves (high-amplitude oscillations < 4.5 Hz) serves as a blueprint of cortical excitability and brain connectivity. Phase-locked auditory stimulation is a promising tool for the modulation of ongoing brain activity during sleep; however, its underlying mechanisms remain unknown. Here, eighteen healthy young adults were measured with high-density electroencephalography in three experimental conditions; one with no stimulation, one with up- and one with down-phase stimulation; ten participants were included in the analysis. We show that up-phase auditory stimulation on a right prefrontal area locally enhances cortical involvement and promotes traveling by increasing the propagating distance and duration of targeted small-amplitude waves. On the contrary, down-phase stimulation proves more efficient at perturbing large-amplitude waves and interferes with ongoing traveling by disengaging cortical regions and interrupting high synchronicity in the target area as indicated by increased traveling speed. These results point out different underlying mechanisms mediating the effects of up- and down-phase stimulation and highlight the strength of traveling wave analysis as a sensitive and informative method for the study of connectivity and cortical excitability alterations.

Automatic Detection of Microsleep Episodes With Deep Learning.

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.

Homeostatic response to sleep restriction in adolescents.

The high prevalence of chronic sleep restriction in adolescents underscores the importance of understanding how adolescent sleep is regulated under such conditions. One component of sleep regulation is a homeostatic process: if sleep is restricted, then sleep intensity increases. Our knowledge of this process is primarily informed by total sleep deprivation studies and has been incorporated in mathematical models of human sleep regulation. Several animal studies, however, suggest that adaptation occurs in chronic sleep restriction conditions, showing an attenuated or even decreased homeostatic response. We investigated the homeostatic response of adolescents to different sleep opportunities. Thirty-four participants were allocated to one of three groups with 5, 7.5, or 10 h of sleep opportunity per night for five nights. Each group underwent a protocol of nine nights designed to mimic a school week between two weekends: two baseline nights (10 h sleep opportunity), five condition nights (5, 7.5, or 10 h), and two recovery nights (10 h). Measures of sleep homeostasis (slow-wave activity and slow-wave energy) were calculated from frontal and central EEG derivations and compared to predictions derived from simulations of the homeostatic process of the two-process model of sleep regulation. Only minor differences were found between empirical data and model predictions, indicating that sleep homeostasis is preserved under chronic sleep restriction in adolescents. These findings improve our understanding of effects of repetitive short sleep in adolescents.

Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment.

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.

Microsleep episodes in the borderland between wakefulness and sleep.

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.

Automatic detection of microsleep episodes with feature-based machine learning.

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.

Response to chronic sleep restriction, extension, and subsequent total sleep deprivation in humans: adaptation or preserved sleep homeostasis?

Sleep is regulated by a homeostatic process which in the two-process model of human sleep regulation is represented by electroencephalogram slow-wave activity (SWA). Many studies of acute manipulation of wake duration have confirmed the precise homeostatic regulation of SWA in rodents and humans. However, some chronic sleep restriction studies in rodents show that the sleep homeostatic response, as indexed by SWA, is absent or diminishes suggesting adaptation occurs. Here, we investigate the response to 7 days of sleep restriction (6 hr time in bed) and extension (10 hr time in bed) as well as the response to subsequent total sleep deprivation in 35 healthy participants in a cross-over design. The homeostatic response was quantified by analyzing sleep structure and SWA measures. Sleep restriction resulted primarily in a reduction of rapid eye movement (REM) sleep. SWA and accumulated SWA (slow-wave energy, SWE) were not much affected by sleep extension/restriction. The SWA responses did not diminish significantly in the course of the intervention and did not deviate significantly from the predictions of the two-process model. The response to total sleep deprivation consisted of an increase in SWA and rise rate of SWA and SWE and did not differ between the two conditions. The data show that changes in sleep duration within an ecologically relevant range have a marked effect on REM sleep and that SWA responds in accordance with predictions based on a saturating exponential increase during wake and an exponential decline in sleep of homeostatic sleep pressure during both chronic sleep restriction and extension.

The Effect of a Slowly Rocking Bed on Sleep.

Rocking movements appear to affect human sleep. Recent research suggested a facilitated transition from wake to sleep and a boosting of slow oscillations and sleep spindles due to lateral rocking movements during an afternoon nap. This study aimed at investigating the effect of vestibular stimulation on sleep onset, nocturnal sleep and its potential to increase sleep spindles and slow waves, which could influence memory performance. Polysomnography was recorded in 18 males (age: 20-28 years) during three nights: movement until sleep onset (C1), movement for 2 hours (C2), and one baseline (B) without motion. Sleep dependent changes in memory performance were assessed with a word-pair learning task. Although subjects preferred nights with vestibular stimulation, a facilitated sleep onset or a boost in slow oscillations was not observed. N2 sleep and the total number of sleep spindles increased during the 2 h with vestibular stimulation (C2) but not over the entire night. Memory performance increased over night but did not differ between conditions. The lack of an effect might be due to the already high sleep efficiency (96%) and sleep quality of our subjects during baseline. Nocturnal sleep in good sleepers might not benefit from the potential facilitating effects of vestibular stimulation.

Vigilance and wake EEG architecture in simulated hyperammonaemia: a pilot study on the effects of L-Ornithine-L-Aspartate (LOLA) and caffeine.

UNLABELLED: Hyperammonaemia/mild hepatic encephalopathy (HE) can be simulated by the oral administration of a so-called amino acid challenge (AAC). This study sought to assess the effects of the AAC alone and in combination with either ammonia-lowering [L-ornithine-L-aspartate (LOLA)] or vigilance-enhancing medication (caffeine). Six patients with cirrhosis (5 males; 61.3 ± 9.2 years; 5 Child A, 1 Child B) and six healthy volunteers (5 males; 49.8 ± 10.6 years) were studied between 08:00 and 19:00 on Monday of three consecutive weeks. The following indices were obtained: hourly capillary ammonia, hourly subjective sleepiness, paper & pencil/computerized psychometry and wake electroencephalography (EEG) at 12:00, i.e. at the time of the maximum expected effect of the AAC. RESULTS: On average, patients had worse neuropsychological performance and slower EEG than healthy volunteers in all conditions but differences did not reach significance. In healthy volunteers, the post-AAC increase in capillary ammonia levels was contained by both the administration of LOLA and of caffeine (significant differences between 10:00 and 14:00 h). The administration of caffeine also resulted in a reduction in subjective sleepiness and in the amplitude of the EEG on several frontal/temporal-occipital sites (p < 0.05; paired t-test). Changes in ammonia levels, subjective sleepiness and the EEG in the three conditions were less obvious in patients. In conclusion, both LOLA and caffeine contained the AAC-induced increase in capillary ammonia, especially in healthy volunteers. Caffeine also counteracted the AAC effects on sleepiness/EEG amplitude. The association of ammonia-lowering and vigilance-enhancing medication in the management of HE is worthy of further study.