Awakening to sleep disorders in Europe: Survey on education, knowledge and treatment competence of European residents and neurologists.

OBJECTIVES: Sleep-wake disorders are common in the general population and in most neurological disorders but are often poorly recognized. With the hypothesis that neurologists do not get sufficient training during their residency, the Young European Sleep Neurologist Association (YESNA) of the European Academy of Neurology (EAN) performed a survey on postgraduate sleep education. METHODS: A 16-item questionnaire was developed and distributed among neurologists and residents across European countries. Questions assessed demographic, training and learning preferences in sleep disorders, as well as a self-evaluation of knowledge based on five basic multiple-choice questions (MCQs) on sleep-wake disorders. RESULTS: The questionnaire was completed by 568 participants from 20 European countries. The mean age of participants was 31.9 years (SD 7.4 years) and was composed mostly of residents (73%). Three-quarters of the participants reported undergraduate training in sleep medicine, while fewer than 60% did not receive any training on sleep disorders during their residencies. Almost half of the participants (45%) did not feel prepared to treat neurological patients with sleep problems. Only one-third of the participants correctly answered at least three MCQs. Notably, 80% of participants favoured more education on sleep-wake disorders during the neurology residency. CONCLUSIONS: Education and knowledge on disorders in European neurological residents is generally insufficient, despite a strong interest in the topic. The results of our study may be useful for improving the European neurology curriculum and other postgraduate educational programmes.

COVID-19 and neurological training in Europe: from early challenges to future perspectives.

The worldwide SARS-CoV-2 pandemic is dramatically affecting health systems with consequences also for neurological residency training. Here we report early experiences and challenges that European neurologists and residents faced. The breadth of the pandemic and the social restrictions induced substantial modifications in both inpatient and outpatient clinical care and academic activities as well, adversely affecting our residency training. On the other hand we see also opportunities, such as gaining more clinical and professional skills. All these drastic and sudden changes lead us to reconsider some educational aspects of our training program that need to be improved in order to better prepare the neurologists of the future to manage unexpected and large emergency situations like the one we are living in these days. A reconsideration of the neurological training program could be beneficial to guarantee high standard level of the residency training in this period and beyond.

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.

Predictive Power of Interictal Epileptiform Discharges in Fitness-to-Drive Evaluation.

BACKGROUND AND OBJECTIVES: This study aimed to evaluate and predict the effects of interictal epileptiform discharges (IEDs) on driving ability using simple reaction tests and a driving simulator. METHODS: Patients with various epilepsies were evaluated with simultaneous EEGs during their response to visual stimuli in a single-flash test, a car-driving video game, and a realistic driving simulator. Reaction times (RTs) and missed reactions or crashes (miss/crash) during normal EEG and IEDs were measured. IEDs, as considered in this study, were a series of epileptiform potentials (>1 potential) and were classified as generalized typical, generalized atypical, or focal. RT and miss/crash in relation to IED type, duration, and test type were analyzed. RT prolongation, miss/crash probability, and odds ratio (OR) of miss/crash due to IEDs were calculated. RESULTS: Generalized typical IEDs prolonged RT by 164 ms, compared with generalized atypical IEDs (77.0 ms) and focal IEDs (48.0 ms) (p < 0.01). Generalized typical IEDs had a session miss/crash probability of 14.7% compared with a zero median for focal and generalized atypical IEDs (p < 0.01). Long repetitive bursts of focal IEDs lasting >2 seconds had a 2.6% miss/crash probabilityIED. Cumulated miss/crash probability could be predicted from RT prolongation: 90.3 ms yielded a 20% miss/crash probability. All tests were nonsuperior to each other in detecting miss/crash probabilitiesIED (zero median for all 3 tests) or RT prolongations (flash test: 56.4 ms, car-driving video game: 75.5 ms, simulator 86.6 ms). IEDs increased the OR of miss/crash in the simulator by 4.9-fold compared with normal EEG. A table of expected RT prolongations and miss/crash probabilities for IEDs of a given type and duration was created. DISCUSSION: IED-associated miss/crash probability and RT prolongation were comparably well detected by all tests. Long focal IED bursts carry a low risk, while generalized typical IEDs are the primary cause of miss/crash. We propose a cumulative 20% miss/crash risk at an RT prolongation of 90.3 ms as a clinically relevant IED effect. The IED-associated OR in the simulator approximates the effects of sleepiness or low blood alcohol level while driving on real roads. A decision aid for fitness-to-drive evaluation was created by providing the expected RT prolongations and misses/crashes when IEDs of a certain type and duration are detected in routine EEG.

The diagnostic value of sleep and vigilance tests in central disorders of hypersomnolence.

STUDY OBJECTIVES: This retrospective cross-sectional observational study explored the diagnostic value of selected sleep and vigilance tests (SVT) beyond the multiple sleep latency test to differentiate between various central disorders of hypersomnolence (CDH) and fatigue syndromes. METHODS: Data from patients who underwent the multiple sleep latency test and at least one additional SVT were extracted from the Bern sleep database (1997-2018). One thousand three hundred fifty-two patients with a CDH (106 narcolepsy type 1, 90 narcolepsy type 2, 119 idiopathic hypersomnia, 192 nonorganic hypersomnia, 205 insufficient sleep syndrome), fatigue syndromes (n = 183), and a subgroup of patients with sleep apnea (n = 457) were analyzed. Classification based on SVT parameters was compared with the final clinical diagnosis serving as a reference. RESULTS: An overall model predicted the final diagnosis in 49.5% of patients. However, for the pairwise differentiation of two clinically suspected diagnoses, many SVT parameters showed a sensitivity and specificity above 70%. While the overall discrimination power of the multiple sleep latency test was slightly better than the one of the maintenance of wakefulness test, the latter differentiated best between narcolepsy and idiopathic hypersomnia with prolonged sleep need. Disproportionally poor results in reaction tests (e.g. steer clear test), despite comparable or lower sleepiness levels (SLAT, WLAT), were valuable for differentiating nonorganic hypersomnia from idiopathic hypersomnia/sleep insufficiency syndrome. CONCLUSION: This study demonstrates how the combination of a careful clinical assessment and a selection of SVTs can improve the differentiation of CDH, whereas it was not possible to establish an overall prediction model based on SVTs alone.

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.

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.

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.

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.

Sleepiness and sleepiness perception in patients with Parkinson's disease: a clinical and electrophysiological study.

STUDY OBJECTIVES: The main objective of the study was to assess the prevalence, the severity, and the daytime course of excessive daytime sleepiness (EDS) in advanced Parkinson's disease (PD) and to explore how people with PD perceive the degree and onset of their sleepiness during objective sleepiness tests. In addition, the occurrence of early-onset rapid eye movement (REM) periods (sleep-onset REM periods [SOREMPs]) in PD was assessed. METHODS: We analyzed data from 46 people with PD (26 males, mean age 63.5 years, mean UPDRS-III-OFF 34.7). The sleep-wake assessment included Epworth sleepiness scale (ESS), Karolinska sleepiness scale (KSS), and objective (polysomnography, multiple sleep latency test [MSLT], and maintenance of wakefulness tests [MWT]) measures. RESULTS: Subjective (ESS ≥ 10) and objective (mean sleep latency, MSL < 5 min in MSLT) EDS were present in 43% and 41% of patients, respectively. The MSL in MSLT and MWT remained unchanged throughout the day and significantly correlated with KSS during the trial but not with KSS shortly before it. In MWT, about one-fourth of patients failed to signal their sleepiness before falling asleep. SOREMPs, usually (83%) arising from NREM1 or wake, were recorded in 24% of the patients. People with SOREMPs had significantly lower MSL in MSLT and MWT and higher AHI compared with those without SOREMPs. CONCLUSIONS: Patients with PD exhibit daylong increased EDS but they underestimate its degree and often fail to signal its onset. SOREMPs in PD have a "narcoleptic" character in sleep-stage sequencing and are associated with the presence of sleep-disturbed breathing. These results add to our understanding of sleepiness and sleepiness perception in PD and have important implications for its diagnosis and management.

Driving simulators in the clinical assessment of fitness to drive in sleepy individuals: A systematic review.

Road traffic injuries are projected to be the leading cause of death for those aged between 15 and 29 years by the year 2030, and sleepiness is estimated to be the underlying cause in up to 15-20% of all motor vehicle accidents. Sleepiness at the wheel is most often caused by socially induced sleep deprivation or poor sleep hygiene in otherwise healthy individuals, medical disorders, or the intake of drugs. Validated methods for objectifying sleepiness are urgently sought, particularly in the context of driving. Based on the assumption that the identification and treatment of sleepiness, and its causes, may prevent motor vehicle accidents, driving simulators are a seemingly promising diagnostic tool. Despite the rising use of these in research, the reliability of their conclusions in healthy sleepy individuals and patients is still unclear. This systematic review aims to evaluate the practical value of driving simulators in a clinical environment when judging fitness to drive in sleepy individuals. From the 1674 records screened, 12 studies in sleepy individuals containing both simulated and real driving data were included. In general, simulated driving did not reliably predict real-life motor vehicle accidents, and especially not on an individual level, despite the moderate relationship between simulated and real-road test driving performance. The severity of sleepiness is most likely not the critical factor leading to motor vehicle accidents, but rather the perception of sleepiness. The self-perception of sleepiness related impairment, and risky behaviour while at the wheel should also be considered as key influencing factors.

Subjective perception of sleepiness in a driving simulator is different from that in the Maintenance of Wakefulness Test.

OBJECTIVE: To test whether sleep-deprived, healthy subjects who do not always signal spontaneously perceived sleepiness (SPS) before falling asleep during the Maintenance of Wakefulness Test (MWT) would do so in a driving simulator. METHODS: Twenty-four healthy subjects (20-26 years old) underwent a MWT for 40 min and a driving simulator test for 1 h, before and after one night of sleep deprivation. Standard electroencephalography, electrooculography, submental electromyography, and face videography were recorded simultaneously to score wakefulness and sleep. Subjects were instructed to signal SPS as soon as they subjectively felt sleepy and to try to stay awake for as long as possible in every test. They were rewarded for both "appropriate" perception of SPS and staying awake for as long as possible. RESULTS: After sleep deprivation, seven subjects (29%) did not signal SPS before falling asleep in the MWT, but all subjects signalled SPS before falling asleep in the driving simulator (p <0.004). CONCLUSIONS: The previous results of an "inaccurate" SPS in the MWT were confirmed, and a perfect SPS was shown in the driving simulator. It was hypothesised that SPS is more accurate for tasks involving continuous feedback of performance, such as driving, compared to the less active situation of the MWT. Spontaneously perceived sleepiness in the MWT cannot be used to judge sleepiness perception while driving. Further studies are needed to define the accuracy of SPS in working tasks or occupations with minimal or no performance feedback.