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The widely used guidelines for sleep staging were developed for the visual inspection of electrophysiological recordings by the human eye. As such, these rules reflect a limited range of features in these data and are therefore restricted in accurately capturing the physiological changes associated with sleep. Here we present a novel analysis framework that extensively characterizes sleep dynamics using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their time-series features, without relying on established scoring conventions. The resulting sleep structure overlapped substantially with that defined by visual scoring. However, we also observed discrepancies between our approach and traditional scoring. This divergence principally stemmed from the extensive characterization by hctsa features, which captured distinctive time-series properties within the traditionally defined sleep stages that are overlooked with visual scoring. Lastly, we report time-series features that are highly discriminative of stages. Our framework lays the groundwork for a data-driven exploration of sleep sub-stages and has significant potential to identify new signatures of sleep disorders and conscious sleep states.

Oculomotor abnormalities are one of the cardinal clinical features of progressive supranuclear palsy (PSP). Vertical saccadic slowing is an early sign of PSP. The association between oculomotor abnormalities and sleep architecture has not been studied so far.To study the association of oculomotor abnormalities of PSP with the sleep stages by using video polysomnography (vPSG).This was a cross-sectional single-center study. Twenty-two patients with PSP and 15 age and gender-matched controls were recruited. Saccades, vestibulo-ocular reflex, and optokinetic nystagmus were assessed and graded clinically in all patients and one overnight vPSG was done in all cases.Vertical saccades, upward more than downwards, were affected in all cases. While horizontal saccades were normal only in 41% of cases. Vertical optokinetic nystagmus (OKN) was affected in all cases. Horizontal OKN was normal in 36% of patients. The vertical upward saccades had a negative correlation with N1% and duration (r = -0.418; p = 0.05, r = -0.457; p = 0.03), N3% and duration (r = -0.486; p = 0.02, r = -0.510; p = 0.01), REM% (r = -0.449; p = 0.04), total sleep time (r = -0.487; p = 0.02) and sleep efficiency (r = -0.444; p = 0.04). There was a positive correlation between horizontal OKN and sleep onset latency (r = 0.432; p = 0.05).Vertical saccadic restriction in PSP has significant negative correlation with total sleep time and sleep efficiency. The oculomotor and sleep abnormalities in PSP are probably interlinked and their assessment is useful in determining the characteristics of the disease.

The visual scoring of gold standard polysomnography (PSG) is known to present inter- and intra-scorer variability. Previously, Somno-Art Software, a cardiac based sleep scoring algorithm, has been validated in comparison to 2 expert visual PSG scorers. The goal of this research is to evaluate the performances of the algorithm against a pool of scorers. Sixty PSG and actimetry recording nights, representative of clinical practice (healthy subjects and patients suffering from obstructive sleep apnea [OSA], insomnia or major depressive disorder), were scored by 5 different sleep scoring centers and by the Somno-Art Software. Intra-class correlation coefficient (ICC) and Wilcoxon Signed-Rank Test were calculated between each scorer and the average value of the 6 scorers, including Somno-Art Software. In addition, epoch-by-epoch agreement between scorers were analyzed. Somno-Art Software estimation of sleep efficiency, wake, N1+N2, N3 and REM sleep fit within the interscorer range for the full dataset and the subgroups, except for underestimating N3 sleep in OSA patients. Additionally, Somno-Art Software overestimated sleep latency compared to the average scoring for insomniacs (+4.7 ± 1.6min). On the full dataset, Somno-Art Software had good (0.75 ICC0.90) or excellent (ICC0.90) ICC scores for all sleep parameters except N3 sleep (moderate score, 0.50 ICC0.75). For the 4-stages epoch-by-epoch agreement, Somno-Art Software was slightly below that of the visual scorers except for the healthy sub-group where an overlap was demonstrated. Somno-Art Software sleep scoring shows a good interscorer reliability in the range of the 5 visual polysomnography scorers.

Analysis of electroencephalogram (EEG) is a crucial diagnostic criterion for many sleep disorders, of which sleep staging is an important component. Manual stage classification is a labor-intensive process and usually suffered from many subjective factors. Recently, more and more computer-aided techniques have been applied to this task, among which deep convolutional neural network has been performing well as an effective automatic classification model. Despite some comprehensive models have been developed to improve classification results, the accuracy for clinical applications has not been reached due to the lack of sufficient labeled data and the limitation of extracting latent discriminative EEG features. Therefore, we propose a novel hybrid manifold-deep convolutional neural network with hyperbolic attention. To overcome the shortage of labeled data, we update the semi-supervised training scheme as an optimal solution. In order to extract the latent feature representation, we introduce the manifold learning module and the hyperbolic module to extract more discriminative information. Eight subjects from the public dataset are utilized to evaluate our pipeline, and the model achieved 89% accuracy, 70% precision, 80% sensitivity, 72% f1-score and kappa coefficient of 78%, respectively. The proposed model demonstrates powerful ability in extracting feature representation and achieves promising results by using semi-supervised training scheme. Therefore, our approach shows strong potential for future clinical development.

Behavioral states naturally alternate between wakefulness and the sleep phases rapid eye movement and nonrapid eye movement sleep. Waking and sleep states are complex processes that are elegantly orchestrated by spatially fine-tuned neurochemical changes of neurotransmitters and neuromodulators including glutamate, acetylcholine, γ-aminobutyric acid, norepinephrine, dopamine, serotonin, histamine, hypocretin, melanin concentrating hormone, adenosine, and melatonin. However, as highlighted in this brief overview, no single neurotransmitter or neuromodulator, but rather their complex interactions within organized neuronal ensembles, regulate waking and sleep states. The neurochemical pathways presented here are aimed to provide a conceptual framework for the understanding of the effects of currently used sleep medications.

Background/Objective Delayed sleep-wake phase disorder (DSWPD) is a chronic condition with a multifactorial etiology that primarily affects adolescents, significantly influencing their quality of life. In clinical practice, the contribution of intrinsic and behavioral factors is difficult to determine. The aim of our study was to compare data from clinical interviews, sleep diaries, actigraphy and nocturnal polysomnography (PSG) in a cohort of adolescents with DSWPD, and to assess psychiatric/neurodevelopmental comorbidity. Patients and methods Thirty-one patients (22 male; mean age 15.4±2.2 years, range 12−19 years) with a diagnosis of DSWPD based on detailed history, sleep diary and actigraphy underwent nocturnal polysomnography (PSG), neurological, psychological and psychiatric examination. Results Attention deficit hyperactivity disorder (ADHD) was present in 14 cases (45%), specific learning difficulties in 9 (29%), and mood disorder (anxiety/depression) in 16 patients (52%). PSG revealed sleep-onset delay in only 12 (38%) cases. No differences in clinical data or psychiatric comorbidity between the group with sleep delay and the group with normal sleep onset were detected. Decreased total sleep time, sleep efficiency, REM sleep and prolonged REM sleep latency were observed in patients with delayed sleep onset. Conclusion PSG showed delayed sleep timing in only 38% of patients with a diagnosis of DSWPD based on diagnostic criteria of the International Classification of Sleep Disorders (ICSD-3). We suggest that PSG can provide useful information regarding the prevailing etiology (biological versus behavioral) if dim light melatonin onset testing is not available.

Sleep plays a major role in neuronal survival and guiding the fetal brain's development. Preterm infants in the neonatal intensive care unit are exposed to numerous external stimuli that can severely disrupt their sleep/wake patterns. Currently, almost no behavioral classification scales are validated for preterm infants. This study aims to develop a new, easy-to-use, validated visual sleep stage classification system for preterm infants with a gestational age between 25 and 37 weeks.The Behavioral Sleep stage classification for Preterm Infants (BeSSPI) consists of four sleep-wake stages; active sleep (AS), quiet sleep (QS), intermediate sleep (IS) and wake (W), which are classified using seven items. Items include eye movements, body movements, facial movements, vocalizations, heart rate, respiratory pattern and activity level.69 preterm infants were observed (24 + 6-36 + 0 weeks GA at birth; 25 + 2-36 + 6 weeks PMA at observation; 57.3% male). Across all 69 infants, the BeSSPI was based on 10,922 min of observed behavior, with 4264 min AS (38.83%), 2873 min QS (26.16%), 2887 min IS (26.29%), and 957 min W (8.72%). For the final BeSSPI, an interrater agreement of κ = 0.80 was reached. Additionally, construct, content, face validity, and expert validity were carefully assessed and deemed satisfactory.We developed a method to evaluate sleep-wake stages that is simple for all neonatal healthcare providers to learn and use. The BeSSPI is of high reliability and validity. Furthermore, it can be used in all preterm age-groups. Therefore, this novel instrument may improve rigor and reproducibility for future preterm sleep research.

This study aimed to evaluate the glymphatic system function in patients with obstructive sleep apnea (OSA) compared to healthy controls using diffusion tensor imaging (DTI) with the perivascular space (DTI-ALPS) method. Our hypothesis is that patients with OSA may have glymphatic system dysfunction, which is correlated with OSA severity.We enrolled 24 patients with OSA and 24 healthy controls. All participants underwent DTI magnetic resonance imaging (MRI) using the same 3T MRI scanner, and we calculated the DTI-ALPS index from the DTI. We evaluated the differences in the DTI-ALPS index between patients with OSA and healthy controls. In addition, we conducted a correlation analysis between the DTI-ALPS index and clinical characteristics.The DTI-ALPS index was significantly different between the groups. The DTI-ALPS in patients with OSA was significantly lower than in healthy controls (1.30450 vs. 1.61600, p = 0.0006). Furthermore, the DTI-ALPS index was significantly negatively correlated with the apnea-hypopnea index in sleep stage N (r = -0.427, p = 0.042) and oxygen desaturation index during sleep N (r = -0.497, p = 0.036).We successfully demonstrated glymphatic system dysfunction in patients with OSA. In addition, glymphatic system dysfunction is well correlated with OSA severity, especially during sleep stage N. Thus, these findings can explain the effects of OSA on increased risk of developing dementia and highlight the importance of OSA treatment.

Arousals are common, sudden and transient elevations of the vigilance level during normal sleep, but arousal-associated behaviors have not yet been studied.We aimed to describe the duration as well as motor and autonomic patterns associated with arousals across sleep stages in normal subjects.The spontaneous arousals of 25 healthy young adults were randomly analyzed on polysomnography with body- and face-oriented video cameras. The duration of the heart rate response as well as the frequency, amplitude, speed, body segment and semiology of associated movements were measured.Among 624 arousals (258 in N2, 140 in N3 and 226 in REM sleep), REM sleep arousals had the shortest duration, and N3 arousals were associated with greater heart rate acceleration. Movements and behaviors (mostly involving the head and neck, then the upper limbs, with rare eyes opening and no turning in bed) were frequent during arousals (69.4% during N2 sleep, 89.3% during N3 and 93.8% during REM sleep). Arousals more frequently included ample, prolonged and whole-body movements during N3 sleep and fast movements and facial expressions during REM sleep. During N2 arousals, chewing was the most prevalent behavior. Some movements resembled orientation and comfort behaviors (flexing/rotating the neck and trunk, scratching, pulling the sheets, rubbing the nose, yawning, smiling, frowning and speaking), whereas others resembled sleep-associated automatisms (swallowing, chewing).In contrast with previous assumptions, most arousals are associated with movements. The type of movements suggests that arousal is an intermediary state between wakefulness and sleep.

Neurologic disorders impact the ability of the brain to generate sleep, wake, and circadian functions. - Preexisting or de novo sleep-wake-circadian pathologies are generally underdiagnosed in neurologic patients despite their major impact on onset, evolution, and outcome of neurologic disorders. - Neurologic disorders are frequently accompanied by sleep-wake EEG changes. Extensive brain damage can lead to the absence of measurable differentiation between sleep and wakefulness (status dissociatus). - New technologies will facilitate early detection and (long-term) monitoring of neurologic patients and the optimization of their clinical management., Sleep Medicine Clinics, 16 (4)

Sleep stage scoring is important to determine sleep structure in preclinical and clinical research. The aim of this study was to develop an automatic sleep stage classification system for mice with a new deep neural network algorithm. For the purpose of base feature extraction, wake-sleep and rapid eye movement (REM) and non- rapid eye movement (NREM) models were developed by extracting defining features from mouse-derived electromyogram (EMG) and electroencephalogram (EEG) signals, respectively. The wake-sleep model and REM-NREM sleep model were integrated into three different algorithms including a rule-based integration approach, an ensemble stacking approach, and a multimodal with fine-tuning approach. The deep learning algorithm assessing sleep stages in animal experiments by the multimodal with fine-tuning approach showed high potential for increasing accuracy in sleep stage scoring in mice and promoting sleep research.

Sleep scoring is one of the primary tasks for the classification of sleep stages using electroencephalogram (EEG) signals. It is one of the most important diagnostic methods in sleep research and must be carried out with a high degree of accuracy because any errors in the scoring in the patient’s sleep EEG recordings can cause serious problems. The aim of this research is to develop a new automatic method for detecting the most important characteristics in sleep stage 2 such as k-complexes based on multi-domain features. In this study, each EEG signal is divided into a set of segments using a sliding window technique. Based on extensive experiments during the training phase, the size of the sliding window is set to 0.5 s (s). Then a set of statistical, fractal, frequency and non-linear features are extracted from each epoch based on the time domain, Katz's algorithm, power spectrum density (PSD) and tunable Q-factor wavelet transform (TQWT). As a result, a vector of twenty-two features is obtained to represent each EEG segment. In order to detect k-complexes, the extracted features were analysed for their ability to detect the k-complex waveforms. Based on the analysis of the features, twelve out of twenty-two features are selected and forwarded to a least square support vector machine (LS-SVM) classifier to identify k-complexes in EEG signals. A set of various classification techniques of K-means and extreme learning machine classifiers are used to compare the obtained results and to evaluate the performance of the proposed method.The experimental results showed that the proposed method, based on multi-domain features, achieved better recognition results than other methods and classifiers. An average accuracy, sensitivity and specificity of 97.7 %, 97 %, and 94.2 % were obtained, respectively, with the CZ-A1 channel according to the R&K standard. The experimental results with high classification performance demonstrated that the technique can help doctors optimize the diagnosis and treatment of sleep disorders.

Background Cyclic alternating pattern (CAP) is the electroencephalogram (EEG) pattern described as a marker of sleep instability and assessed by NREM transient episodes in sleep EEG. It has been associated with brain maturation. The aim of this review was to evaluate the normative data of CAP parameters according to the aging process in healthy subjects through a systematic review and meta-analysis. Methods Two authors independently searched databases using PRISMA guidelines. Discrepancies were reconciled by a third reviewer. Subgroup analysis and tests for heterogeneity were conducted. Results Of 286 studies, 10 submitted a total of 168 healthy individuals to CAP analysis. Scoring of CAP can begin at 3 months of life, when K-complexes, delta bursts, or spindles can be recognized. Rate of CAP increased with age, mainly during the first 2 years of life, then decreased in adolescence, and increased in the elderly. The A1 CAP subtype and CAP rate were high in school-aged children during slow-wave sleep (SWS). A1 CAP subtypes were significantly more numerous in adolescents compared with other groups, while the elderly showed the highest amounts of A2 and A3 CAP subtypes. Our meta-analysis registered the lowest CAP rate in infants younger than 2 years old and the highest in the elderly. Conclusions This review summarized the normative data of CAP in NREM sleep during the aging process. The CAP rate increased with age and sleep depth, especially during SWS. Parameters of CAP may reflect gender hormonal effects and neuroplasticity. More reports on CAP subtypes are needed for their reference values establishment.

The brain mechanisms by which we transition from sleep to a conscious state remain largely unknown in humans, partly because of methodological challenges. Here we study a pre-existing dataset of waking up participants originally designed for a study of dreaming (Horikawa, Tamaki, Miyawaki, & Kamitani, 2013) and suggest that suddenly awakening from early sleep stages results from a two-stage process that involves a sequence of cortical and subcortical brain activity. First, subcortical and sensorimotor structures seem to be recruited before most cortical regions, followed by fast, ignition-like whole-brain activation-with frontal regions engaging a little after the rest of the brain. Second, a comparably slower and possibly mirror-reversed stage might take place, with cortical regions activating before subcortical structures and the cerebellum. This pattern of activation points to a key role of subcortical structures for the initiation and maintenance of conscious states.

Sleep-related complaints are so common in older adults that it may be difficult to distinguish whether the complaint is a consequence of normal aging or a disease process. The elderly are more likely to have common medical problems that affect sleep, and the most common sleep problems, including sleep apnea and insomnia, are more prevalent in this demographic. This article briefly describes normal sleep in general, the clinical assessment of sleep complaints, and expected changes with aging, with an overview of the epidemiology of insomnia and sleep apnea in this age group.

Background Sleep disturbances are features of Parkinson's disease (PD), that can already occur before PD diagnosis. The most investigated prodromal PD sleep disorder is REM sleep behavior disorder (RBD). The relation between other polysomnographic (PSG) alterations and the prediagnostic stages of PD, however, is less clear. Methods We performed a retrospective case–control study to characterize polysomnographic alterations in PD and prediagnostic PD. We included 63 PD subjects (33 subjects that underwent a video-PSG before PD diagnosis [13 with and 20 without RBD] and 30 subjects that underwent a PSG after PD diagnosis) and 30 controls. PSGs were analyzed for sleep stages, different RSWA variables, body position, arousals, periodic limb movements, and REM density. Results Higher subscores of all RSWA variables were observed in subjects with PD and prediagnostic PD (with and without RBD). Total RSWA, tonic RSWA and chin RSWA severity were significant predictors for all PD and prediagnostic PD groups. Our study also shows a higher percentage of nocturnal supine body position in all PD and prediagnostic PD groups. Supine body position percentage is the highest in the PD group and has a positive correlation with time since diagnosis. Conclusions These findings suggest that increased total, tonic and chin RSWA as well as nocturnal supine body position are already present in prediagnostic PD, independently of RBD status. Prospective longitudinal studies are necessary to confirm the additional value of these PSG abnormalities as prodromal PD biomarkers.

Objective We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to include data from non-rapid eye movement (NREM) and rapid eye movement (REM) sleep (von Ellenrieder et al., 2020), and the present work extends our methods to those data. Methods Normalized amplitude spectra on semi-logarithmic axes from all four arousal states (wake, N2, N3 and REM) were averaged region-wise and fitted to a multi-component Gaussian distribution. A reduced model comprising five key parameters per brain region was color-coded on to cortical surface models. Results The lognormal Gaussian mixture model described the iEEG accurately from all brain regions, in all sleep-wake states. There was smooth variation in model parameters as sleep and wake states yielded to each other. Specific observations unrelated to the model were that the primary cortical areas of vision, motor function and audition, in addition to the hippocampus, did not participate in the ‘awakening’ of the cortex during REM sleep. Conclusions Despite the significant differences in the appearance of the time-domain EEG in wakefulness and sleep, the iEEG in all arousal states was successfully described by a parametric spectral model of low dimension. Significance Spectral variation in the iEEG is continuous in space (across different cortical regions) and time (stage of circadian cycle), arguing for a ‘continuum’ hypothesis in the generative processes of sleep and wakefulness in human brain.

Objectives To study the prevalence of benign EEG variants (BEVs) in the sleep–wake cycle among 1163 consecutive patients. Methods Prospective, observational EEG study using the 10–20 system with systematically two additional anterior-temporal electrodes. Depending on clinical indications, other electrodes were added. REM sleep identification was based on its characteristic EEG grapho-elements and rapid eye movements, clearly detectable with the additional anterior-temporal and fronto-polar electrodes due to eye proximity. The video-EEG monitoring duration was between 24 hours and eight days. Results We identified 710 patients (61%) with BEVs. Positive occipital sharp transients of sleep (POSTs) were observed in 36.4% of participants, mu rhythm in 22.4%, lambda waves in 16.7%, wicket spikes (WS) in 15%, 14- and 6-Hz positive bursts in 8.3%, benign sporadic sleep spikes (BSSS) in 3.3%, rhythmic mid-temporal theta burst of drowsiness (RMTD) in 2.15%, midline theta rhythm in 2.1% and six-Hz spike and wave (SW) bursts in 0.1%. WS and RMTD were present during wakefulness, NREM (14.1%, 1.3%, respectively) and REM sleep (3.3%, 1.1%, respectively). Mu rhythm was also observed during NREM (1.5%) and REM sleep (7.7%). Fourteen- and 6-Hz positive bursts were present during NREM (4.5%) and REM sleep (6.5%). BSSS and six-Hz SW bursts were only observed during NREM sleep. Conclusions The prevalence of BEVs is much higher than current estimates. POSTs and WS can no longer be considered as unusual patterns but physiological patterns of NREM sleep. RMTD and mu rhythm may be observed during NREM and REM sleep.

Study objectives: Unobtrusive monitoring of sleep and sleep disorders in children presents challenges. We investigated the possibility of using Ultra-Wide band (UWB) radar to measure sleep in children. Methods: Thirty-two children scheduled to undergo a clinical polysomnography participated; their ages ranged from 2 months to 14 years. During the polysomnography, the children's body movements and breathing rate were measured by an UWB-radar. A total of 38 features were calculated from the motion signals and breathing rate obtained from the raw radar signals. Adaptive boosting was used as machine learning classifier to estimate sleep stages, with polysomnography as gold standard method for comparison. Results: Data of all participants combined, this study achieved a Cohen's Kappa coefficient of 0.67 and an overall accuracy of 89.8% for wake and sleep classification, a Kappa of 0.47 and an accuracy of 72.9% for wake, rapid-eye-movement (REM) sleep, and non-REM sleep classification, and a Kappa of 0.43 and an accuracy of 58.0% for wake, REM sleep, light sleep and deep sleep classification. Conclusion: Although the current performance is not sufficient for clinical use yet, UWB radar is a promising method for non-contact sleep analysis in children.

Supine sleeping position and obesity are well-known risk factors for obstructive sleep apnea (OSA) and modulate the risk for OSA-related daytime symptoms. Although respiratory event durations are associated with OSA-related severe health consequences, it is unclear how sleeping position, obesity, and daytime sleepiness are associated with respiratory event durations during REM and NREM sleep. We hypothesize that irrespective of the apnea-hypopnea index (AHI), respiratory event durations differ significantly between various OSA subgroups during REM and NREM sleep.One night in-lab polysomnographic recordings were retrospectively analyzed from 1910 untreated suspected OSA patients. 599 patients (AHI ≥ 5) were included in study and divided into subgroups based on positional dependency, BMI, and daytime sleepiness (Epworth Sleepiness Scale and Multiple Sleep Latency Test). Differences in total hypopnea time (THT), total apnea time (TAT), and total apnea-hypopnea time (TAHT) within REM and NREM sleep between the subgroups were evaluated.During REM sleep, positional OSA patients had lower THT (OR = 0.952, p 0.001) and TAHT (OR = 0.943, p 0.001) than their non-positional counterparts. Compared to normal-weight patients (BMI25 kg/mRegardless of the AHI, respiratory event durations vary significantly between OSA sub-groups during REM and NREM sleep. Therefore, to personalize OSA severity estimation the diagnosis should be tailored based on patient's demographics, clinical phenotype, and PSG characteristics.

Background Researchers have proposed that impaired sleep may be a causal link in the progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Several recent findings suggest that enhancing deep sleep (N3) may improve neurological health in persons with MCI, and buffer the risk for AD. Specifically, Transcranial Electrical Stimulation (TES) of frontal brain areas, the inferred source of the Slow Oscillations (SOs) of N3 sleep, can extend N3 sleep duration and improve declarative memory for recently learned information. Recent work in our laboratory using dense array Electroencephalography (dEEG) localized the sources of SOs to anterior limbic sites – suggesting that targeting these sites with TES may be more effective for enhancing N3. Methods For the present study, we recruited 13 healthy adults (M = 42 years) to participate in three all-night sleep EEG recordings where they received low level (0.5 mA) TES designed to target anterior limbic areas and a sham stimulation (placebo). We used a convolutional neural network, trained and tested on professionally scored EEG sleep staging, to predict sleep stages for each recording. Results When compared to the sham session, limbic-targeted TES significantly increased the duration of N3 sleep. TES also significantly increased spectral power in the 0.5–1 Hz frequency band (relative to pre-TES epochs) in left temporoparietal and left occipital scalp regions compared to sham. Conclusion These results suggest that even low-level TES, when specifically targeting anterior limbic sites, can increase deep (N3) sleep and thereby contribute to healthy sleep quality.

Multiple interactions between sleep and the ovarian axis are operative in physiological and pathological conditions. Both estradiol and progesterone contribute to the regulation of specific sleep stages, which in turn contribute to the regulation of pulsatile secretion of gonadotropins. Sleep troubles, in particular sleep deprivation, may be associated with menstrual cycle irregularity, anovulation, and miscarriage. In addition, insufficient sleep favors the development of obesity and insulin resistance, which in turn favors the development of polycystic ovary syndrome (PCOS). Conversely, PCOS is associated with sleep disturbances, in particular with obstructive sleep apnea (OSA), eventually leading to a vicious cycle. Improving sleep quality and duration could provide a cost-effective and patient-centered avenue to reduce incidence and severity of PCOS.

Background In current clinical practice, sleep is manually scored in discrete stages of 30-s duration. We hypothesize that modelling sleep automatically as continuous and dynamic process predicts healthy ageing better than traditional scoring. Methods Sleep electroencephalography of 15 young healthy subjects (aged ≤40 years) was used to train the modelling method. Each 3-s sleep mini-epoch was modelled as a probabilistic combination of wakefulness, light and deep sleep. For 79 healthy sleepers (aged 20–77 years), 15 sleep features were derived from manual traditional scoring (manual features), 7 from the automatic modelling (automatic features) and 24 from a combination of automatic modelling with traditional scoring (combined features). Age was predicted with seven multiple linear regression models with i) manual, ii) automatic, iii) combined, iv) manual + automatic, v) manual + combined, vi) automatic + combined, and vii) manual + automatic + combined sleep features. Using the same seven sleep feature groups, two support vector machine and one random forest classifiers were used to discriminate younger (aged Results The linear model and the classifiers using only manual features achieved the lowest values of adjusted coefficient of determination and classification validation accuracy (adj-R2 = 0.295, ACC = 63.00% ± 16.22%) compared to the ones using automatic (adj-R2 = 0.354, ACC = 65.83% ± 9.39%), combined (adj-R2 = 0.321, ACC = 63.42% ± 8.78%), manual + automatic (adj-R2 = 0.416, ACC = 67.00% ± 8.60%), manual + combined (adj-R2 = 0.355, ACC = 72.17% ± 12.90%), automatic + combined (adj-R2 = 0.448, ACC = 65.92% ± 7.97%), and manual + automatic + combined sleep features (adj-R2 = 0.464, ACC = 70.92% ± 10.33%). Conclusions Continuous and dynamic sleep modelling captures healthy ageing better than traditional sleep scoring.

Objective Electroencephalographic (EEG) changes in patients with NREM parasomnias (NRP) occur in sleep architecture as changes in slow wave sleep or cyclic pattern, which are not considered abnormal. However, abnormalities in EEG in these patients have recently been reported, indicating that EEG patterns in NRPs are not definitive. Moreover, most of the polysomnography (PSG) findings in NRP patients were reported in the adult population requiring data from pediatric population to avoid bias in conclusion. Methods In sum, 39 patients with a NRP were undergone comprehensive assessments including a PSG with additional EEG montages. EEG recordings were evaluated in patients without a history of epilepsy and further compared between pediatric and adult patients. Results Twenty-three (59%) of the patients were pediatric and 77% were male. The mean age was 18.4 (±13.1) years. Of the patients, 19 (49%) had somnambulism, 13 (33%) had confusional arousal and seven (18%) had sleep terrors. Macrostructure of sleep detected by PSG was normal in all patients. After excluding 11 (28%) patients with a positive history of epilepsy, seven (25%) of 28 showed EEG abnormalities within K-complexes in NREM-II stage, six of whom were pediatric patients compared to only one adult (p Conclusion This study showed that patients with NRP may display EEG abnormalities in NREM-II stage. These abnormalities were more frequent in pediatric patients compared to adults. In NRP patients, utmost care should be taken in EEG evaluations to prevent false diagnosis of epilepsy.

Producción Científica, Heart rate variability (HRV) is modulated by sleep stages and apneic events. Previous studies in children compared classical HRV parameters during sleep stages between obstructive sleep apnea (OSA) and controls. However, HRV-based characterization incorporating both sleep stages and apneic events has not been conducted. Furthermore, recently proposed novel HRV OSA-specific parameters have not been evaluated. Therefore, the aim of this study was to characterize and compare classic and pediatric OSA-specific HRV parameters while including both sleep stages and apneic events. A total of 1610 electrocardiograms from the Childhood Adenotonsillectomy Trial (CHAT) database were split into 10-minute segments to extract HRV parameters. Segments were characterized and grouped by sleep stage (wake, W; non-rapid eye movement, NREMS; and REMS) and presence of apneic events (under 1 apneic event per segment, e/s; 1–5 e/s; 5–10 e/s; and over 10 e/s). NREMS showed significant changes in HRV parameters as apneic event frequency increased, which were less marked in REMS. In both NREMS and REMS, power in BW2, a pediatric OSA-specific frequency domain, allowed for the optimal differentiation among segments. Moreover, in the absence of apneic events, another defined band, BWRes, resulted in best differentiation between sleep stages. The clinical usefulness of segment-based HRV characterization was then confirmed by two ensemble-learning models aimed at estimating apnea-hypopnea index and classifying sleep stages, respectively. We surmise that basal sympathetic activity during REMS may mask apneic events-induced sympathetic excitation, thus highlighting the importance of incorporating sleep stages as well as apneic events when evaluating HRV in pediatric OSA., Ministerio de Ciencia, Innovación y Universidades y el Fondo Europeo de Desarrollo Regional (FEDER) under projects (PID2019-104881RB-I00), (PID2020-115468RB-I00), (PDC2021-120775-I00) and (PID2021-126734OB-C21), Sociedad Española de Sueño (SES) en el marco del proyecto “Beca de Investigación SES 2019”, by 'CIBER-Consorcio Centro de Investigación Biomédica en Red- (CB19/01/00012)' a través del 'Instituto de Salud Carlos III' co- financiado con fondos FEDER, así como bajo el proyecto SleepyHeart de la convocatoria de valorización 2020, y por el Gobierno de Aragón (Grupo de Referencia BSICoS T39-20R)., The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002)., Ministerio de Ciencia, Innovación y Universidades, “Ayudas para contratos predoctorales para la Formación de Doctores” grant (PRE2018-085219) and “Ramón y Cajal” (MICIU/FSE) grant (RYC2019-028566-I), Institutos Nacionales de Salud-"Ensayo de adenoamigdalectomía infantil (CHAT)"- (HL083075, HL083129, UL1-RR-024134, UL1 RR024989) and (grant AG061824).

Sleep regulation and functioning may rely on systematic coordination throughout the whole brain, including the cerebellum. However, whether and how interactions between the cerebellum and other brain regions vary across sleep stages remain poorly understood. Here, using simultaneous EEG-fMRI recordings captured from 73 participants during wakefulness and non-rapid eye movement (NREM) sleep, we constructed cerebellar connectivity among intrinsic functional networks with intra-cerebellar, neocortical and subcortical regions. We uncovered that cerebellar connectivity exhibited sleep-dependent alterations: slight differences between wakefulness and N1/N2 sleep and greater changes in N3 sleep than other states. Region-specific cerebellar connectivity changes between N2 sleep and N3 sleep were also revealed: general breakdown of intra-cerebellar connectivity, enhancement of limbic-cerebellar connectivity and alterations of cerebellar connectivity with spatially specific neocortices. Further correlation analysis showed that functional connectivity between the cerebellar Control II network and regions (including the insula, hippocampus, and amygdala) correlated with delta power during N3 and beta power during N2 sleep. These findings systematically reveal altered cerebellar connectivity among intrinsic networks from wakefulness to deep sleep and highlight the potential role of the cerebellum in sleep regulation and functioning.

Sleep disturbances are highly prevalent across all stages of Bipolar Disorder. Despite a wealth of research on the neurophysiological features of sleep in this population, progress in this field has been slow. We aimed to review the literature on sleep electroencephalography (EEG) studies in Bipolar Disorder, considering sleep architecture and microstructural oscillatory activity. We included a total of 22 studies: six on sleep during manic episodes, seven during depressive episodes, seven in euthymic patients and two in high-risk individuals. The most consistent findings were increased SOL and REM density across all stages of the disorder. Only two studies reported a reduced spindle count during bipolar depression and euthymia, respectively. Although not specific for Bipolar Disorder, SOL and REM density have been repeatedly found to be increased across all stages of illness in this population. Whereas the former reflects a difficulty initiating sleep, the latter can be considered a neurophysiological signature of patients' overall reduced sleep need, independent of illness stage.

Background Obstructive sleep apnea (OSA) is associated with increased cardiac arrhythmia and sudden cardiac death. We recently developed a new method (neuECG) to noninvasively measure electrocardiogram and skin sympathetic nerve activity (SKNA). Objective The purpose of this study was to test the hypothesis that SKNA measured during sleep study is higher in patients with OSA than in those without OSA. Methods We prospectively recorded neuECG and polysomnography in 26 patients undergoing a sleep study. Sleep stages were scored into rapid eye movement (REM), and non-REM sleep stages 1 (N1), 2 (N2), and 3 (N3). Average voltage of skin sympathetic nerve activity (aSKNA) and SKNA burst area were calculated for quantification. Apnea/hypopnea index (AHI) >5 per hour was used to diagnose OSA. Results There was a positive correlation (r = 0.549; P = .018) between SKNA burst area and the arousal index in OSA but not in the control group. aSKNA during sleep was 0.61 ± 0.09 μV in OSA patients (n = 18) and 0.53 ± 0.04 μV in control patients (n = 8; P = .025). Burst area was 3.26 (1.90–4.47) μV·s/min in OSA patients and 1.31 (0.67–1.94) μV·s/min in control (P = .047). More apparent differences were found during N2, when the burst area in OSA (3.06 [1.46–5.52] μV·s/min) was much higher than that of the control (0.89 [0.79–1.65] μV·s/min; P = .03). Conclusion OSA patients have higher SKNA activity than control patients, with the most pronounced differences observed during N2. Arousal at the end of apnea episodes is associated with large SKNA bursts. Overlaps of aSKNA and SKNA burst area between groups suggest that not all OSA patients have increased sympathetic tone.

Purpose Our aim was to investigate the value of rapid eye movement (REM) during prolonged scalp video-electroencephalography (VEEG) in the localization of epileptogenic foci for patients with focal epilepsy. Method We retrospectively studied a total of 59 patients with focal epilepsy and 31 of 59 received surgery. We assessed localization of interictal epileptiform discharges (IEDs) during REM, non–rapid eye movement sleep (NREM) and wakefulness to compare with the localization of ictal EEG, clinical semiology, magnetic resonance imaging (MRI) and positron emission tomography (PET) and stereo-electroencephalogram (SEEG). We graded postoperative follow-up outcome according to Engel criteria to further verify the accuracy of localization of epileptogenic foci in REM-IEDs. NREM-IEDs and Wakefulness-IEDs. Stepwise multiple logistic regression was carried out to assess for independent association of good prognosis with REM accurate localization, temporal lobe epilepsy and MRI accurate localization. Results Clinical semiology was concordant to REM‐IEDs in 40 patients (72.7 %), NREM‐IEDs in 27 (49.1 %), and Wakefulness‐IEDs in 25 (45.5 %). MRI lesion was concordant with REM‐IEDs in 35 patients (81.4 %), Wakefulness‐IEDs in 26 (60.5 %), and NREM‐IEDs in 25 (58.1 %). PET localization was concordant with REM‐IEDs in 20 patients (76.9 %), Wakefulness‐IEDs and NREM‐IEDs in 11 (42.3 %). SEEG localization was concordant with REM-IEDs in 15 patients (65.2 %), Wakefulness‐IEDs in 10 (43.5 %), and NREM‐IEDs in 8 (34.8 %). Thirty-one patients received surgery, and 30 (96.8 %) of them achieved good seizure control (Engel I-III). The surgical site was concordant with REM‐IEDs in 23 (74.2 %), Wakefulness‐IEDs and NREM‐IEDs in 14 (45.2 %). In addition, the accuracy of REM-IEDs localization in temporal epilepsy (90 %) was higher than that extra-temporal epilepsy (45.5 %). REM accurate localization of epileptogenic foci was an independent factor contributing to good prognosis (P = 0.025, OR = 12.368). Conclusions Compared with NREM-IEDs and Wakefulness-IEDs, REM-IEDs had most value for localization of epileptogenic foci in patients with focal epilepsy. REM-IEDs- accurate localization of epileptogenic foci was an independent factor contributing to good prognosis for postsurgical patients with focal epilepsy.

Sleep-related problems have been frequently reported in neurodevelopmental disorders, with special emphasis in Autism Spectrum Disorder (ASD) and Attention Deficit/Hyperactivity Disorder (ADHD). The aim of the present study is to conduct a systematic review and meta-analysis on sleep disturbances in adults with ASD and/or ADHD (PROSPERO's CRD42019132916). PubMed and PsycINFO were searched for studies reporting data on sleep objective/subjective measures, as well as prevalence data of sleep disorders, in adults with ASD and/or ADHD. A manual search was conducted throughout reference lists of eligible studies. A total of 1126 studies and 66 references were identified by electronic and manual searches, respectively. Of these, 42 studies were included in the meta-analysis. Results showed that both disorders share a similar sleep-impaired profile with higher sleep onset latency, poorer sleep efficiency, greater number of awakenings during sleep, and a general lower self-perceived sleep quality compared with healthy controls. A higher proportion of N1 sleep was found in ASD participants, while a greater Periodic Limb Movements in Sleep is specific in ADHD adults. More studies are needed, especially those directly comparing ASD and ADHD participants. Controlling for medication, intellectual disability, and concurrent psychiatric disorders is mandatory.

Study objectives Sleep is associated with a reduction in ventilation and an increase in upper airway resistance (UAR) in patients with obstructive sleep apnea (OSA). However, there is no consensus on the standard for assessment of UAR and therefore it is important to develop a method to reliably assess UAR in patients with OSA. The purpose of the present study is to determine whether the ratio of neural respiratory drive (NRD) to flow can be used to assess changes in UAR in OSA during sleep. Methods A total of 24 patients (21 men) with OSA and 10 normal subjects (6 males) were studied. The UAR was assessed by the ratio of NRD to flow, which measured by esophageal pressure (Poes), diaphragm electromyography (EMGdi) and superficial diaphragm electromyography (SEMGdi) in various stages including wakefulness, N2 sleep, N2 sleep with snoring, hypopneas, the in the “preapnea” states in OSA versus wakefulness, sleeponset, N2 sleep, N3 sleep in normal subjects. All subjects underwent overnight full polysomnography using standard techniques. Results Our study indicate that UAR was progressively higher from wakefulness to N2 sleep, N2 sleep with snoring, hypopneas, and the in the “preapnea” states in patients with OSA and had obvious difference in statistical significance (p Conclusions It is feasible to use the ratio of neural respiratory drive to flow to assess UAR in patients with OSA during sleep.

Background The minimum narcolepsy criteria “mean sleep latency (MSL) ≤8 min and ≥2 sleep onset rapid eye movement (REM) periods (SOREMPs) on polysomnography (PSG) and the multiple sleep latency test (MSLT),” according to The International Classification of Sleep Disorders, Third Edition (ICSD-3), are not specific to narcolepsy. Recently, the characteristic sleep stage sequences preceding SOREMPs in narcolepsy have received attention, but their diagnostic utility remains unclear. Methods We retrospectively reviewed PSG/MSLT records and chart data for 102 Japanese patients with hypersomnia and at least one SOREMP. We examined the sporadic rates of two sleep stage sequences preceding the SOREMPs—wakefulness or stage 1 to REM (W/S1→R) and stage 2 to REM (S2→R)—comparing these between patient groups with narcolepsy type 1 (N = 28), narcolepsy type 2 (N = 19), and other hypersomnia (N = 55). We also examined the utility of three simple indices using the occurrence of W/S1→R SOREMPs for distinguishing between narcolepsy and other hypersomnia in patients who satisfied the minimum narcolepsy criteria. Results W/S1→R SOREMPs were significantly more frequent in narcolepsy than in other hypersomnia, and this tendency was also observed even in the patients who satisfied the minimum narcolepsy criteria. The three indices had moderate sensitivities and specificities for distinguishing between narcolepsy and other hypersomnia in patients satisfying the minimum narcolepsy criteria. Conclusions The W/S1→R pattern was observed significantly more frequently in narcolepsy than in other hypersomnia, suggesting it may help with differentiating narcolepsy from other hypersomnia in patients demonstrating the narcolepsy criteria, although its ability to do so may be modest.

Sleep-disordered breathing (SDB) is associated with neurocognitive and behavioral dysfunction, and structural brain abnormalities. Near infrared spectroscopy allows a continuous and non-invasive monitoring of brain tissue oxygenation, giving insight in some pathophysiological mechanisms potentially associated with SDB-related neurocognitive dysfunction. The present review summarizes the finding of studies describing brain tissue oxygenation in adults and children with SDB. Contrary to adults, mean nocturnal tissue oxygenation index (TOI) during sleep does not seem to be different in children with SDB as compared to healthy controls. During respiratory events such as apnoeas and hypopnoeas, the decrease in TOI precedes the peripheral, systemic desaturation. The decrease in TOI has been shown to be greater during apnoeas as compared to hypopnoeas, during rapid-eye movement sleep as compared to other sleep stages, in younger children as compared to their older counterparts, and in those with a high apnoea-hypopnoea index as compared with a low apnoea-hypopnoea index. Studies analyzing the association between repetitive changes in TOI and neurocognitive and behavioral dysfunction may help to decipher the pathophysiology of neurocognitive dysfunction associated with SDB in children.

It remains unclear whether neurobiological dysfunction observed in major depressive disorder (MDD) and insomnia is an expression of common or independent bases. The present investigation sought to explore differences in heart rate variability (HRV)—a widely utilized biomarker of neurobiological functioning—among individuals with MDD, insomnia, and healthy controls, while awake and during distinct sleep stages (REM, N2), with the goal of improving our understanding of shared neurobiological factors in depression and insomnia. Participants were 73 adults who underwent home polysomnography. All N2 and REM sleep epochs with a duration greater than or equal to 5 min were identified for HRV analysis. Additionally, a single waking epoch was defined for each participant. From waking to N2 sleep, and waking to REM sleep, changes in HRV indices indicated participants experienced reductions in sympathetic arousal and increases in parasympathetic arousal. Contrary to hypotheses, however, no between group differences were observed in HRV. Though the present findings do not support the hypotheses of a shard neurobiological pathway between MDD and insomnia, more work is warranted to advance our understanding of the neurobiological bases of these common, debilitating, and frequently co-occurring psychiatric conditions, to improve early detection and identify novel intervention targets for these disorders.

Objective Spontaneous K-complexes (SKCs), a hallmark of stage 2 sleep, have been reported to decrease in density in Alzheimer's disease (AD) patients. However, few former studies have explored the alterations in SKC characteristics in the pre-clinical phase of AD—amnestic mild cognitive impairment (aMCI). The aim of our prospective cohort study was to investigate the changing trend in SKC characteristics during the progression of aMCI. Methods SKC density, amplitude and duration were measured in aMCI subjects and normal controls (NC) at two-year follow-up assessments by polysomnography (PSG). In sum, 22 NCs, 25 stable aMCI (sMCI) subjects and 20 progressive aMCI (pMCI) subjects finished the four follow-up PSG assessments, and their data were used for analysis. Results SKC density and amplitude, but not duration, decreased during the follow-up assessments in both NCs and aMCI subjects, but the rate of decrease of these parameters was greater in aMCI subjects. With the progression of aMCI, significant differences in SKC density and amplitude among the three groups were observed, whereas SKC density showed no difference at the early stage of aMCI. The receiver operating characteristic (ROC) curve results demonstrated that SKC density and amplitude could distinguish aMCI subjects from NCs with high specificity and sensitivity. Conclusion Our results suggest that SKCs decrease with ageing and the progression of aMCI, and SKC characteristics may be potential biomarkers for diagnosing aMCI.

(max. 250) Background Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI). Method We analyzed the PSG of 347 patients with chronic insomnia, including 59 with Sleep State Misperception (SSM) and 288 without (INS). 89 good sleepers (GS) were used as controls. PSGs were compared regarding: (1) macroscopic indexes derived from the hypnogram, (2) mesoscopic indexes extracted from the electroencephalographic (EEG) spectrum, (3) sleep microstructure (slow waves, spindles). We used supervised algorithms to differentiate patients from GS. Results Macroscopic features illustrate the insomnia conundrum, with SSM patients displaying similar sleep metrics as GS, whereas INS patients show a deteriorated sleep. However, both SSM and INS patients showed marked differences in EEG spectral components (meso) compared to GS, with reduced power in the delta band and increased power in the theta/alpha, sigma and beta bands. INS and SSM patients showed decreased spectral slope in NREM. INS and SSM patients also differed from GS in sleep microstructure with fewer and slower slow waves and more and faster sleep spindles. Importantly, SSM and INS patients were almost indistinguishable at the meso and micro levels. Accordingly, unsupervised classifiers can reliably categorize insomnia patients and GS (Cohen’s κ =0.87) but fail to tease apart SSM and INS patients when restricting classifiers to micro and meso features ( κ =0.004). Conclusion AI analyses of PSG recordings can help moving insomnia diagnosis beyond subjective complaints and shed light on the physiological substrate of insomnia.

Studies over the last 40 years have mainly investigated sleep structure changes as a result of wake duration, in the frame of the classical sleep regulation theories. However, wake intervals of the same duration can profoundly differ in their intensity, which actually reflects the degree of cognitive and physical activity. Data on how sleep can be modified by wake intensity changes (initially sparse and of little consistence) have become much more substantial, especially in the frame of the intense research debate on sleep-memory relationships. Our aim is to examine the vast repertoire of sleep modifications that depend on waking cognitive manipulations, highlighting the sleep features that appear most affected. By systematically addressing this issue, we want to set the basis for future research exploring both the specific nature of the mechanisms involved and the applicative psychosocial and clinical fall-outs, in terms of possible behavioural interventions for sleep quality improvement.

For automatic sleep stage classification, the existing methods mostly rely on hand-crafted features selected from polysomnographic records. In this paper, the goal is to develop a deep learning-based method by using single channel electroencephalogram (EEG) that automatically exploits the time–frequency spectrum of EEG signal, removing the need for manual feature extraction. The time–frequency RGB color images for EEG signal are extracted using continuous wavelet transform (CWT). The transfer learning of a pre-trained convolution neural network, squeezenet is employed to classify these CWT images into its sleep stages. The proposed method is evaluated using a publicly available Physionet sleep EDFx dataset using single-channel EEG Fpz-Cz channel. Evaluation results show that this method can achieve near state of the art accuracy even using single channel EEG signal.

Background Sleep is vital for normal body functions as sleep disorders can adversely affect a person. Electroencephalographic (EEG) signals indicate brain functions and have characteristic signatures for various sleep stages. These enable the use of EEG as an effective tool for in-depth studies about sleep. Sleep stages are broadly divided as rapid eye movement (REM) and non-rapid eye movement (NREM). NREM is further divided into 3 stages. The objective of the work is to distinguish the given EEG epoch as wake, NREM1, NREM2, NREM3 and REM. DREAMS Subject Database containing 5 EEG channels is used here. This work focuses on utilizing EEG by exploiting variations in inter-dependencies of different brain regions during sleep. New method Covariance matrices of the wavelet-decomposed channels are used to obtain the variations in inter-dependencies. The feature sets are: (1) simple matrix properties(MF) like trace, determinant and norm, (2) eigen-values (E1), (3) eigen-vector corresponding to the largest eigen-value (E2) and (4) tangent vectors obtained using Riemann geometry (RG-TS). The features are input to ensemble classifier with bagging. Subject-specific, All-subjects-combined and Leave-one-subject-out methods of analysis are carried out. Results In all methods of analysis, RG-TS features give maximum accuracy (80.05%, 83.05% and 61.79%), closely followed by E1 (79.49%, 77.14% and 58.34%). Comparison with existing method The proposed method obtains higher and/or comparable accuracy. This work also ensures no biasing of classifier due to unequal class distribution. Conclusion The performances of RG-TS and E1 features reveal that the changes in inter-dependencies of pre-frontal and occipital lobe along with the central lobe can be used to distinguish the different sleep stages.

This paper presents the implementation of deep learning methods for sleep stage detection by using three signals that can be measured in a non-invasive way: heartbeat signal, respiratory signal, and movement signal. Since signals are measurements taken during the time, the problem is seen as time-series data classification. Deep learning methods are chosen to solve the problem are convolutional neural network and long-short term memory network. Input data is structured as a time-series sequence of mentioned signals that represent 30 seconds epoch, which is a standard interval for sleep analysis. The records used belong to the overall 23 subjects, which are divided into two subsets. Records from 18 subjects were used for training the data and from 5 subjects for testing the data. For detecting four sleep stages: REM (Rapid Eye Movement), Wake, Light sleep (Stage 1 and Stage 2), and Deep sleep (Stage 3 and Stage 4), the accuracy of the model is 55%, and F1 score is 44%. For five stages: REM, Stage 1, Stage 2, Deep sleep (Stage 3 and 4), and Wake, the model gives an accuracy of 40% and F1 score of 37%.

Introduction Sleep-related rhythmic movements (SRRMs) are common in young children and become less prevalent with increasing age. When SRRMs significantly interfere with sleep and/or affect daytime functioning, potentially resulting in injury, rhythmic movement disorder (SRRMD) is diagnosed. Objective The aim of our study was to assess clinical comorbidities, types of SRRMs, sleep stage/wakefulness distribution during night, and age-dependence of these parameters. Material and methods In sum, 45 patients (age range 1–26 years, mean age 10.56 ± 6.4 years, 29 men) were clinically examined for SRRMs or SRRMD. Nocturnal polysomnography (PSG) was recorded in 38 patients. To evaluate clinical and sleep comorbidity, the cohort of 38 patients was divided according to age into four groups: (1) younger than 5 years (N = 7), (2) 5–9 years (N = 12), (3) 10–14 years (N = 11), and (4) ≥ 15 years (N = 8). Results A clear relationship between perinatal risk factors and developmental disorders (attention deficit hyperactivity disorder - ADHD, specific learning disability) was found which extended population prevalence at least five times. A total of 62 recordings were evaluated in 38 patients; SRRMs were found in PSG in 31 of 38 patients (82%). No age-dependent correlation between type of SRRMs and sleep stage/wakefulness distribution during the night was observed. However, when all recordings were correlated together, rolling stereotypes occurred more frequently in REM sleep, and rocking stereotypes in superficial NREM sleep. Conclusion Developmental disorders and perinatal risk factors were connected with SRRMs and SRRMD in children and young adults. Rolling movements were significantly associated with REM stage and rocking stereotypes with superficial NREM sleep, independent of age.

Even though sympathetic dominance during the daytime period is well known, currently, scarce data exist on autonomic nervous system (ANS) regulation during sleep in pediatric obesity. We aimed to evaluate sleep cardiac ANS regulation in normal-weight (NW) and overweight and obese (OW) adolescents.In this study, 60 healthy adolescents (15.7 ± 0.7 years) belonging to a birth cohort since infancy were classified based on body mass index percentiles criteria as: OW (N = 27) or NW (N = 33). Sleep was evaluated by polysomnography (PSG) during two consecutive in-lab overnight sessions. Non-rapid eye movement (non-REM) sleep stages (stages 1, 2, and slow-wave sleep [SWS]), rapid eye movement (REM) sleep, and wakefulness (Wake) were scored. R-waves were detected automatically in the electrocardiographic (ECG) signal. An all-night heart rate variability analysis was conducted in the ECG signal, with several time- and frequency-domain measures calculated for each sleep-wake stage. Sleep time was divided into thirds (T1, T2, T3). The analysis was performed using a mixed-effects linear regression model.Sleep organization was comparable except for reduced REM sleep percentage in the OW group (p 0.04). Shorter R-R intervals were found for all sleep stages in the OW group; time-domain measured standard deviation of all R-R intervals (RRSD) was lower during stage 2, SWS and REM sleep (all p 0.05). The square root of the mean of the sum of the squares of differences between adjacent R-R intervals (RMSSD) was also lower only during wake after sleep onset (WASO) in T1 and T3 (p 0.05). The OW group had increased very low- and low-frequency (LF) power during WASO (in T1 and T2), and LF power during stage 2 and REM sleep (in T2). During WASO in the OW group, high-frequency (HF) power was lower (in T1 and T2), and LF/HF ratio was higher (in T2, p 0.007).Several sleep-stage-dependent changes in cardiac autonomic regulation characterized the OW group. As sleep-related ANS balance was disturbed in the absence of concomitant metabolic alterations in this sample of otherwise healthy OW adolescents, their relevance for pediatric obesity should be further explored throughout development.

Objective Conventional polysomnographic recordings reflect brain dynamics associated with sleep architecture. We hypothesized that noninvasive tools like transcranial alternating current stimulation (tACS) and acoustic stimulation (for generating event related potentials [ERPs]) would help to predict sleep stability and provide a window to actively assess brain activity during sleep. Materials and methods Twelve healthy male volunteers participated in the multiple whole-night polysomnography (PSG) recording protocol. Acoustic tones (100 msec duration) were presented throughout night to evaluate ERP during sleep. Furthermore, 30 sec tACS were presented during nonrapid eye movement (NREM) and rapid eye movement (REM) sleep on subsequent two nights without disturbing the subject's sleep. For ERP analysis, event-locked artifact-free epochs from each sleep stage were averaged separately. For tACS analysis, 30 sec prestimulus and poststimulus artifact-free EEG epochs were subjected to bootstrapping-based comparison of power spectral values. Results Acoustic stimulation generated sleep stage-dependent ERP components (N350, N550, and P900) in all participants. The tACS stimulation during NREM sleep (0.75 Hz) increased parietal delta power but decreased frontocentral theta and increased frontal gamma power when delivered during REM sleep (40Hz). These interventions provide details on sleep stability as larger N550-P900 ERP-complex correlated with lower NREM disruptions (Spearman's rho = -0.553; p = 0.049; n = 10) and tACS-related theta power perturbation with higher REM disruptions (Spearman's rho = 0.734; p = 0.030; n = 7). Conclusions Noninvasive brain stimulation approaches such as sleep ERP and sleep tACS are reliable tools to evaluate sleep stability during NREM and REM sleep, respectively, but more large-sample studies are warranted.

K-complexes are important transient bio-signal waveforms in sleep stage 2. Detecting k-complexes visually requires a highly qualified expert. In this study, an efficient method for detecting k-complexes from electroencephalogram (EEG) signals based on fractal and frequency features coupled with an ensemble model of three classifiers is presented. EEG signals are first partitioned into segments, using a sliding window technique. Then, each EEG segment is decomposed using a dual-tree complex wavelet transform (DT-CWT) to a set of real and imaginary parts. A total of 10 sub-bands are used based on four levels of decomposition, and the high sub-bands are considered in this research for feature extraction. Fractal and frequency features based on DT-CWT and Higuchi’s algorithm are pulled out from each sub-band and then forwarded to an ensemble classifier to detect k-complexes. A twelve-feature set is finally used to detect the sleep EEG characteristics using the ensemble model. The ensemble model is designed using a combination of three classification techniques including a least square support vector machine (LS-SVM), k-means and Naive Bayes. The proposed method for the detection of the k-complexes achieves an average accuracy rate of 97.3 %. The results from the ensemble classifier were compared with those by individual classifiers. Comparisons were also made with existing k-complexes detection approaches for which the same datasets were used. The results demonstrate that the proposed approach is efficient in identifying the k-complexes in EEG signals; it yields optimal results with a window size 0.5 s. It can be an effective tool for sleep stages classification and can be useful for doctors and neurologists for diagnosing sleep disorders.

Objective We studied ripple-band (80–200 Hz) high-frequency oscillations in scalp electroencephalogram (EEG) in various pediatric epilepsies featuring sleep-activated spikes, such as epileptic encephalopathy with continuous spike-and-wave during sleep (CSWS) and investigated their characteristics. Methods The subjects were 94 children with epileptic disorders including idiopathic and non-idiopathic CSWS, benign epilepsy with centrotemporal spikes (BECTS), Panayiotopoulos syndrome, other types of focal epilepsies (oFE), and focal spikes without clinical seizures (Latent). We detected ripple oscillations using a semi-automatic detection tool based on localized power increase. Results In the idiopathic CSWS Group, the median ratio of ripples per spike in the initial EEG was 5.73, which was significantly higher than those in the BECTS, Panayiotopoulos syndrome, oFE, and Latent Groups (0.39, 0.02, 0.35, 0, respectively, all with p Conclusions This paper is the first to confirm a high ratio of ripples per spike in CSWS in the largest number of patients to date. Significance The dense generation of ripples, which occurs through a combination of heavy loading of individual spikes with ripples and large numbers of spikes during sleep, characterizes CSWS and might be closely related to the pathophysiology of this epileptic encephalopathy.

RESUMEN Introducción: La somnolencia diurna excesiva (SDE) puede llegar a interferir en el desempeño académico y profesional, debido a que las personas afectadas tienden a quedarse dormidas en situaciones que exigen un alto nivel de atención. Los estudiantes de Medicina representan una población en riesgo de SDE, dada la exigencia académica de numerosas horas de estudio, debido al gran número de créditos por asignatura contenidos en el plan de estudios del programa académico, las prácticas docentes asistenciales y los turnos nocturnos, que pueden generar privación o déficit acumulado del sueño. Por esta razón, es importante estimar la prevalencia de SDE y los factores asociados en estudiantes de Medicina de una institución de educación superior (IES) de Bucaramanga, con el objetivo de implementar estrategias de prevención primaria que disminuyan la presentación de este problema y mejoren la calidad de vida y el desempeño académico de los estudiantes. Material y métodos: Estudio transversal analítico observacional, con una muestra poblacional de 458 estudiantes de Medicina matriculados en el segundo semestre de 2015 en la Universidad Autónoma de Bucaramanga (UNAB), quienes respondieron a 4 cuestionarios: variables sociodemográficas, escala de somnolencia de Epworth, índice de calidad del sueño de Pittsburg (ICSP) e índice de higiene del sueño (IHS). Se realizó el análisis bivariable y multivariable en busca de asociación con SDE. Resultados: Los estudiantes tenían una media de edad de 20,3 arios; de los 458 encuestados, el 62,88% eran mujeres. Se estableció que el 80,75% de los participantes tenían SDE y el 80,55%, una percepción negativa de la calidad del sueño (OR = 1,91;IC95%, 1,11-3,29; p = 0,019). En el análisis multivariable, se encontró que el hecho de estar cursando ciencias clínicas disminuye el riesgo de SDE respecto a quienes estaban cursando el ciclo básico. Además, se observó que una puntuación > 15 en el IHS aumenta de manera significativa el riesgo de padecer SDE. Conclusiones: Aunque es frecuente encontrar SDE en los estudiantes de Medicina, solo un pequeño porcentaje de ellos sufren la forma severa de este trastorno del sueño. Estar cursando asignaturas del ciclo básico se asocia con mayor riesgo de SDE, por lo cual es importante que los comités curriculares de las IES evalúen regularmente la cantidad de horas de trabajo supervisado e independiente que realizan los estudiantes de Medicina. Finalmente, es importante emprender campañas orientadas a mejorar la percepción de riesgo sobre el uso de bebidas energizantes de los estudiantes universitarios y realizar, desde el ingreso al programa académico, recomendaciones sobre los hábitos de higiene del sueño. ABSTRACT Introduction: Excessive daytime sleepiness (EDS) can interfere with academic and professional performance, as affected individuals tend to fall asleep in situations that demand a high level of alertness. Medical students are often a population at risk of suffering from EDS due to the demanding number of study hours, the significant number of credits per subject in the academic curriculum, practical teaching sessions and hospital night shifts, which can lead to sleep deprivation or sleep debt. It is for these reasons that it is important to estimate the prevalence of EDS and its associated factors in medical students of a Higher Education Institution (HEI) in Bucaramanga, in order to implement early prevention strategies to reduce the occurrence of this problem and to improve the students' quality of life and academic performance. Material and methods: An observational, cross-sectional analytical study with a population sample of 458 medical students enrolled in the second semester of 2015 at the Universidad Autonomade Bucaramanga (UNAB), who completed four questionnaires: Sociodemographic Variables, Epworth Sleepiness Scale, Pittsburgh Sleep Quality Index and Sleep Hygiene Index (SHI). A bivariate and multivariate analysis was performed to identify any correlations with EDS. Results: Mean student age was 20.3 years and 62.88% of the 458 respondents were women. We were able to establish that 80.75% of participants suffered from EDS and 80.55% had a negative perception of their sleep quality (OR=1.91; 95% CI, 1.11-3.29; p =0.019). In the multivariate analysis, it was found that the risk of EDS is lower in the clinical sciences than in the basic cycle. Furthermore, it was noted that a score higher than 15 in the Sleep Hygiene Index significantly increases the risk of suffering from EDS. Conclusions: Although EDS is very common in medical students, only a small percentage present the most severe form of this sleep disorder. Being enrolled in basic cycle subjects is associated with a higher risk of suffering EDS, so it is important for the curriculum committees of higher education institutions to regularly evaluate the number of hours of supervised and independent work performed by medical students. Finally, it is important to implement campaigns aimed at improving university students' perception of the risk of taking energy drinks and to establish sleep hygiene recommendations from the start of the academic programme.

It is very important to have good quality sleep, which can affect aspects such as memory consolidation, emotional regulation, learning, physical development, and quality of life. Diagnosing human sleep quality and problems quickly and accurately is an important issue for human well-being. Therefore, many automatic sleep scoring methods have been proposed. However, the methods have been developed using sleep data from different individuals or groups. The accuracies of these proposed methods might decrease, due to existing individual differences. In this study, the self-attention generative adversarial network (SAGAN) was applied as an advanced data augmentation technique to propose an improved personalized automatic sleep scoring classification. First, the spectrograms were converted from electroencephalography (EEG). Then, SAGAN was used to generate synthesized spectrograms for each subject. Finally, the real and synthesized spectrograms of each subject were utilized to train a personalized classifier. The averaged accuracy and standard deviation of the proposed method are 95.74% and 3.78%, respectively. Compared to the classifier trained with all subjects' training data, the average accuracy increased by 8.08%. The results proved that the generated spectrograms significantly improved the performance of the personalized automatic sleep scoring classification. The contributions of the proposed method were that made the medical staff and subjects save massive medical resources and time for manual recording and scoring.

Healthy sleep is essential for the rejuvenation of the body and helps in maintaining good health. Many people suffer from sleep disorders that are characterized by abnormal sleep patterns. Automated assessment of such disorders using biomedical signals has been an active subject of research. Electroencephalogram (EEG) is a popular diagnostic used in this regard. We consider a widely-used publicly available database and process the signals using the Fourier decomposition method (FDM) to obtain narrowband signal components. Statistical features extracted from these components are passed on to machine learning classifiers to identify different stages of sleep. A novel feature measuring the non-stationarity of the signal is also used to capture salient information. It is shown that classification results can be improved by using multi-channel EEG instead of single-channel EEG data. Simultaneous utilization of multiple modalities, such as Electromyogram (EMG), Electrooculogram (EOG) along with EEG data leads to further enhancement in the obtained results. The proposed method can be efficiently implemented in real-time using fast Fourier transform (FFT), and it provides better classification results than the other algorithms existing in the literature. It can assist in the development of low-cost sensor-based setups for continuous patient monitoring and feedback.

Background and objective The recognition of many sleep related pathologies highly relies on an accurate classification of sleep stages. Clinically, sleep stages are usually labelled by sleep experts through visually inspecting the whole-night polysomnography (PSG) recording of patients, wherein electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) play the dominant role. Developing an automatic sleep staging system based on multi-channel physiological signals could relieve the burden of manual labeling by experts, and obtain reliable and repeatable recognition results as well. Methods In this work, we find the correlation between the spatial covariance matrices of multi-channel signals and their corresponding sleep stages. Based on that, we propose two novel sleep stage classification methods based on the features extracted from the covariance matrices of multi-channel signals. Sleep stages are classified using a minimum distance classifier according to their corresponding covariance matrices mapped on Riemannian manifolds. An alternative way to classify these covariance matrices is to represent the features of covariance matrices on the tangent space of Riemannian manifolds and classify them with an ensemble learning classifier. After any of these classification methods, a rule-free refinement process is utilized to further optimize the classification results. Results On the MASS dataset that includes 61 whole-night PSG recordings, both two methods provide satisfactory classification results while the one based on tangent space projection has better performance. On average, an accuracy of 0.812 and a Cohen's Kappa coefficient of 0.722 are obtained under leave-one-subject-out cross validation, using EEG, EOG and EMG signals. Meanwhile, the most effective combinations of EEG channels for sleep staging have been found in this work. Conclusions The correlation between spatial covariance matrices of multi-channel signals and their corresponding sleep stages have been found. Features based on that are used for sleep stage classification, and experimental results show the superior performance of proposed methods compared to state-of-the-art works. Results of this work are expected to provide a new vision for dealing with multi-channel or multi-modal signal processing tasks in various applications.

Objective Fragmentary myoclonus (FM) is a polysomnographic motor phenomenon of unknown clinical relevance. This study investigates FM prevalence, gender differences, sleep stage distribution and association with clinical factors using recently introduced advanced FM scoring criteria. Methods We analyzed polysomnographic recordings of 178 patients of a mixed sleep-disorder patient cohort. FM indices (FMI) of newly introduced 25 μV (FMI25) and standard 50 μV (FMI50) amplitude cut-offs were calculated. Results FMI25 and FMI50 were higher in men compared to women. FMI were higher during wakefulness and lower during S3 compared to all other sleep stages, with stronger effects in men compared to women. FMI25 was correlated with higher age, lower mean oxygen saturation, lower sleep efficiency, higher periodic limb movement (PLM) index, shorter sleep period time and higher arousal index. Linear regression showed that age predicted higher FMI25 in both males and females. Additionally, higher arousal index predicted higher FMI25 in women only. FMI were not associated with the presence of sleep-related breathing disorders. Conclusions We suggest FM represents a ubiquitous motor phenomenon occurring spontaneously during relaxed wakefulness and sleep, primarily in men and with advanced age. Significance In women, particularly FMI25 may be a surrogate marker for more frequent arousals and sleep fragmentation.