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.

Multiple sleep latency test and polysomnography in patients with central disorders of hypersomnolence.

A multiple sleep latency test (MSLT) with occurrence of sleep onset REM periods (SOREMP) is considered one of the central diagnostic criteria for narcolepsy according to the International Classification of Sleep Disorders, but its sensitivity and specificity have been questioned. This study aims to describe MSLT and polysomnography (PSG) findings, including frequency and distribution of SOREMP during the day, in a large cohort of patients with central disorders of hypersomnolence (CDH). We retrospectively analyzed electrophysiological data from MSLT and PSG in 370 consecutive patients with narcolepsy type 1 (NT1, n = 97), type 2 (NT2, n = 31), idiopathic hypersomnia (IH, n = 48), nonorganic hypersomnia (NOH, n = 116) and insufficient sleep syndrome (ISS, n = 78). NT1 and NT2 patients had a significantly shorter mean Sleep Latency (mSL) and REM-Latency (REML) in MSLT and PSG. SOREMP occurred more frequently in narcoleptic vs. non-narcoleptic patients in MSLT and PSG. Occurrence of 3 or more SOREMP in MSLT and a SOREMP in PSG had a very high specificity and positive predictive value (98%/96% and 100% respectively), however relatively low sensitivity (65% and 45% respectively). NT1 more than NT2 patients have shorter mSL and more frequent SOREMP in MSLT and shorter SL as well as REML during nocturnal PSG. Increasing numbers of SOREMP in MSLT and especially SOREMP during PSG increase specificity on the expense of sensitivity in diagnosing narcolepsy. Therefore, frequency of SOREMP in MSLT naps and PSG can help to discriminate but not clearly separate narcoleptic from non-narcoleptic patients.

New 2013 incidence peak in childhood narcolepsy: more than vaccination?

Increased incidence rates of narcolepsy type-1 (NT1) have been reported worldwide after the 2009-2010 H1N1 influenza pandemic (pH1N1). While some European countries found an association between the NT1 incidence increase and the H1N1 vaccination Pandemrix, reports from Asian countries suggested the H1N1 virus itself to be linked to the increased NT1 incidence. Using robust data-driven modeling approaches, that is, locally estimated scatterplot smoothing methods, we analyzed the number of de novo NT1 cases (n = 508) in the last two decades using the European Narcolepsy Network database. We confirmed the peak of NT1 incidence in 2010, that is, 2.54-fold (95% confidence interval [CI]: [2.11, 3.19]) increase in NT1 onset following 2009-2010 pH1N1. This peak in 2010 was found in both childhood NT1 (2.75-fold increase, 95% CI: [1.95, 4.69]) and adulthood NT1 (2.43-fold increase, 95% CI: [2.05, 2.97]). In addition, we identified a new peak in 2013 that is age-specific for children/adolescents (i.e. 2.09-fold increase, 95% CI: [1.52, 3.32]). Most of these children/adolescents were HLA DQB1*06:02 positive and showed a subacute disease onset consistent with an immune-mediated type of narcolepsy. The new 2013 incidence peak is likely not related to Pandemrix as it was not used after 2010. Our results suggest that the increased NT1 incidence after 2009-2010 pH1N1 is not unique and our study provides an opportunity to develop new hypotheses, for example, considering other (influenza) viruses or epidemiological events to further investigate the pathophysiology of immune-mediated narcolepsy.

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.

The Swiss Narcolepsy Scale (SNS) and its short form (sSNS) for the discrimination of narcolepsy in patients with hypersomnolence: a cohort study based on the Bern Sleep-Wake Database.

Previous studies reported high sensitivity and specificity of the Swiss Narcolepsy Scale (SNS) for the diagnosis of narcolepsy type 1. We used data from the Bern Sleep-Wake Database to investigate the discriminating capacity of both the SNS and the Epworth Sleepiness Scale (ESS) to identify narcolepsy type 1 and type 2 in patients with central disorders of hypersomnolence (CDH) or sleepy patients with obstructive sleep apnea (OSA). In addition, we aimed to develop a simplified version of the SNS. We created the two-item short-form SNS (sSNS), based on the discriminative capability of the models including all possible combinations of the five questions of the SNS. Using the previously published co-efficiencies, we confirmed the high capacity of the SNS in identifying narcolepsy type 1. The updated SNS (based on new co-efficiencies and cutoff) and the sSNS showed high capacity and were both superior to ESS in identifying narcolepsy type 1. The sSNS correlated significantly with the SNS (r = - 0.897, p < 0.001). No scale showed sufficient discrimination for narcolepsy type 2. This is the largest cohort study that confirms the discriminating power of SNS for narcolepsy type 1 in patients with hypersomnolence and the first study to assess its discriminative power for narcolepsy type 2. The easy-to-use and easy-to-calculate short-form scale has a high discriminating power for narcolepsy type 1 and may be used as screening tool, especially among general practitioners, to identify patients and accelerate their referral to a center of expertise.

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.

[Narcolepsy and Other 'Central Disorders of Hypersomnolence']. / Narkolepsie und andere «zentrale Hypersomnolenzen¼.

Narcolepsy and Other 'Central Disorders of Hypersomnolence' Abstract. 'Excessive daytime sleepiness', 'hypersomnia' in the sense of prolonged sleep need, 'tiredness' or 'fatigue' are frequent complaints in primary care, requiring a careful separation in view of a correct diagnostic. After exclusion of various internistic and neurologic causes by clinical and laboratory investigations and after exclusion of sleep apnoea syndrome or other causes of disturbed sleep by polysomnography, the ambiguous group of 'Central Disorders of Hypersomnolence' remains, including narcolepsy with and without cataplexy, idiopathic hypersomnia, and non-organic hypersomnia. Due to the important therapeutic consequences, these diseases must be differentiated and distinguished from insufficient sleep and from chronic fatigue syndrome, often requiring interdisciplinary diagnostics including objective assessment of the reported complaints.

T cells in patients with narcolepsy target self-antigens of hypocretin neurons.

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.

Characteristic Fluctuations Around Stable Attractor Dynamics Extracted from Highly Nonstationary Electroencephalographic Recordings.

Since the discovery of electrical activity of the brain, electroencephalographic (EEG) recordings constitute one of the most popular techniques of brain research. However, EEG signals are highly nonstationary and one should expect that averages of the cross-correlation coefficient, which may take positive and negative values with equal probability, (almost) vanish when estimated over long data segments. Instead, we found that the average zero-lag cross-correlation matrix estimated with a running window over the whole night of sleep EEGs, or of resting state during eyes-open and eyes-closed conditions of healthy subjects shows a characteristic correlation pattern containing pronounced nonzero values. A similar correlation structure has already been encountered in scalp EEG signals containing focal onset seizures. Therefore, we conclude that this structure is independent of the physiological state. Because of its pronounced similarity across subjects, we believe that it depicts a generic feature of the brain dynamics. Namely, we interpret this pattern as a manifestation of a dynamical ground state of the brain activity, necessary to preserve an efficient operational mode, or, expressed in terms of dynamical system theory, we interpret it as a "shadow" of the evolution on (or close to) an attractor in phase space. Nonstationary dynamical aspects of higher cerebral processes should manifest in deviations from this stable pattern. We confirm this hypothesis through a correlation analysis of EEG recordings of 10 healthy subjects during night sleep, 20 recordings of 9 epilepsy patients, and 42 recordings of 21 healthy subjects in resting state during eyes-open and eyes-closed conditions. In particular, we show that the estimation of deviations from the stationary correlation structures provides a more significant differentiation of physiological states and more homogeneous results across subjects.

Rapid eye movements sleep as a predictor of functional outcome after stroke: a translational study.

Study Objectives: Sleep disturbances are common in acute stroke patients and are linked with a negative stroke outcome. However, it is also unclear which and how such changes may be related to stroke outcome. To explore this link, we performed a sleep electroencephalogram (EEG) study in animals and humans after ischemic stroke. Methods: (1) Animal study: 12 male rats were assigned to two groups: ischemia (IS) and sham surgery (Sham). In both groups, sleep architecture was investigated 24 h before surgery and for the following 3 days. (2) Human study: 153 patients with ischemic stroke participating in the SAS-CARE prospective, multicenter cohort study had a polysomnography within 9 days after stroke onset. Functional stroke outcome was assessed by the modified Rankin Scale (mRS) at hospital discharge (short-term outcome) and at a 3-month follow-up (long-term outcome). Results: (1) Animal study: rapid eye movement (REM) sleep was significantly reduced in the IS group compared to the Sham group. (2) Human study: patients with poor short-term functional outcome had a reduction of REM sleep and prolonged REM latency during the acute phase of stroke. REM latency was the only sleep EEG variable found to be significantly related to short- and long-term functional impairment in a multiple linear regression analysis. Conclusions: Acute ischemic stroke is followed by a significant reduction of REM sleep in animals and humans. In humans, this reduction was linked with a bad stroke outcome; in addition, REM latency was found to be an independent predictor of stroke evolution. Potential explanations for this role of REM sleep in stroke are discussed. Clinical Trial Registration: http://clinicaltrials.gov. Unique identifier: NCT01097967.

Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of poorly defined diseases with excessive daytime sleepiness as a core feature. Due to the considerable overlap of symptoms and the rarity of the diseases, it is difficult to identify distinct phenotypes of CH. Machine learning (ML) can help to identify phenotypes as it learns to recognize clinical features invisible for humans. Here we apply ML to data from the huge European Narcolepsy Network (EU-NN) that contains hundreds of mixed features of narcolepsy making it difficult to analyze with classical statistics. Stochastic gradient boosting, a supervised learning model with built-in feature selection, results in high performances in testing set. While cataplexy features are recognized as the most influential predictors, machine find additional features, e.g. mean rapid-eye-movement sleep latency of multiple sleep latency test contributes to classify NT1 and NT2 as confirmed by classical statistical analysis. Our results suggest ML can identify features of CH on machine scale from complex databases, thus providing 'ideas' and promising candidates for future diagnostic classifications.

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.

K-band Doppler radar for contact-less overnight sleep marker assessment: a pilot validation study.

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.

Sleep-wake functions and quality of life in patients with subthalamic deep brain stimulation for Parkinson's disease.

OBJECTIVES: 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. PATIENTS AND METHODS: 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). RESULTS: 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. CONCLUSION: 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.

Post Tick-Borne Encephalitis Virus Vaccination Narcolepsy with Cataplexy.

STUDY OBJECTIVES: Narcolepsy with cataplexy (NC) is a chronic neurological disorder thought to result from an altered immune response based on a genetic predisposition coupled with environmental factors. Pandemrix vaccination has been reported to increase the risk of narcolepsy. We aimed at identifying other vaccines associated with the onset of narcolepsy. METHODS: Case series and retrospective database study. RESULTS: We identified four cases of NC following a tick-borne encephalitis (TBE) vaccination with FSME Immun. Additional four cases could be detected in the database of the Paul-Ehrlich-Institut, Federal Institute for Vaccines and Biomedicines in Germany. CONCLUSIONS: Our findings implicate TBE vaccination as a potential additional environmental factor for the development of NC and add additional evidence for an immunological mechanism in the pathogenesis of the disease.