Wake Stages Research Articles

Stimulation-Evoked Resonant Neural Activity in the Subthalamic Nucleus Is Modulated by Sleep.

Deep brain stimulation is a treatment for advanced Parkinson's disease and currently tuned to target motor symptoms during daytime. Parkinson's disease is associated with multiple nocturnal symptoms such as akinesia, insomnia, and sleep fragmentation, which may require adjustments of stimulation during sleep for best treatment outcome. There is a need for a robust biomarker to guide stimulation titration across sleep stages. This study aimed to investigate whether evoked resonant neural activity (ERNA) is modulated by sleep. We recorded local field potentials from the subthalamic nucleus of four Parkinson's patients with externalized electrodes while applying single stimulation pulses to investigate the effect of sleep on ERNA. We found that ERNA features change with wakefulness and sleep stages and are correlated with canonical frequency bands and heart rate. Given that ERNA modulates with sleep, it could be used as a robust marker for automatic stimulation titration during sleep. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Drooling in developmentally normal children: a review.

Drooling or saliva spillage has been explored widely among children with neurodevelopmental conditions. Yet, the approach to drooling in an otherwise developmentally normal child remains unexplored, as it is regarded as self-limiting. Nonetheless, drooling beyond age 4 in the awake stage should raise concern. This narrative review aims to shed light on drooling in developmentally normal children, also known as 'healthy droolers', and the available evidence on its management. Most notable factors causing saliva spillage include poor oral-motor control and impaired oral sensation. Delayed saliva acquisition may be an early indicator of developmental or intellectual delay. Drooling impairs both the children's and parents' overall quality of life significantly. Healthy droolers can be managed by simple behavioural therapy and reassurance.

Dynamic thermal comfort skin temperatures of young adults during sleep and the effects of gender and sleep stage

This study aims to investigate the local skin temperatures of young adults during sleep in a thermally comfortable indoor environment (thermal sensation vote of −1 to +1). Differences in the thermal comfort skin temperatures (Tsk,tc) caused by changes in gender and sleep stage (i.e., wake, REM, light, deep) are discussed. 8-Hour sleep tests were conducted involving 40 subjects (20/20 males/females). Their local skin temperatures at nine sites were recorded every minute, forming a physiological database of thermal comfort. Analysis on the collected data demonstrated significant (p < 0.05) variations in local Tsk,tc both between genders and between distinct stages of sleep. Throughout the 8-h sleep, females had higher Tsk,tc than males. Notably, this difference is most pronounced at the abdomen region, where the Tsk,tc was 0.43 °C higher for females. Additionally, females had a wide range of mean Tsk,tc from 34.08 °C to 34.75 °C, while males had a narrow range from 33.80 °C to 34.25 °C. Furthermore, the Tsk,tc during the wake stage is significantly lower compared to that in the other three stages, implying skin temperature could be used as an indicator to distinguish sleep stages. Also, the variation in Tsk,tc across sleep stages is more pronounced among females than males. This research reveals gender-specific and sleep stage-related disparities in local Tsk,tc. These findings provide a scientific basis for set-point skin temperatures in thermal regulation models for sleeping. Besides, the development of personalized sleep comfort systems and indoor environment would benefit from current findings, which improves human sleep thermal comfort and quality.

Impaired Sleep-Dependent Memory Consolidation in Pediatric Narcolepsy Type 1.

Disrupted nighttime sleep (DNS) is common in pediatric Narcolepsy type 1, yet its cognitive impact is unknown. As N2 sleep spindles are necessary for sleep-dependent memory consolidation, we hypothesized that Narcolepsy Type 1 impairs memory consolidation via N2 sleep fragmentation and N2 sleep spindle alterations. We trained 28 pediatric Narcolepsy Type 1 participants and 27 healthy controls (HC) on a spatial declarative memory task before a nocturnal in-lab polysomnogram and then gave them a cued recall test upon awakening in the morning. We extracted wake and sleep stage bout numbers and N2 spindle characteristics from the polysomnogram and conducted mixed model analysis of sleep-dependent memory consolidation to identify group differences. Narcolepsy Type 1 participants had shorter N2 bout durations and associated shorter N2 spindles vs. HC, but other N2 spindle features were similar. Narcolepsy Type 1 participants had worse memory performance post-sleep than HCs after adjusting for age and gender (mean memory consolidation HC: -3.1% ± 18.7, NT1: -15.6 ± 24.8, main effect group x time of testing F=5.3, p=0.03). We did not find significant relationships between sleep-dependent memory consolidation and N2 spindle characteristics. Notably, increased N1% was associated with worse sleep-dependent memory consolidation with results driven by the Narcolepsy Type 1 group. Sleep-dependent memory consolidation is mildly impaired in youth with Narcolepsy Type 1 and findings may be attributed to increases in N1 sleep. Further studies are needed to determine if these findings are generalizable and reversible with sleep-based therapies.

Phase-Amplitude Coupling in Theta and Beta Bands: A Potential Electrophysiological Marker for Obstructive Sleep Apnea.

Phase-amplitude coupling (PAC) between the phase of low-frequency signals and the amplitude of high-frequency activities plays many physiological roles and is involved in the pathological processed of various neurological disorders. However, how low-frequency and high-frequency neural oscillations or information synchronization activities change under chronic central hypoxia in OSA patients and whether these changes are closely associated with OSA remains largely unexplored. This study arm to elucidate the long-term consequences of OSA-related oxygen deprivation on central nervous system function. : We screened 521 patients who were clinically suspected of having OSA at our neurology and sleep centers. Through polysomnography (PSG) and other clinical examinations, 103 patients were ultimately included in the study and classified into mild, moderate, and severe OSA groups based on the severity of hypoxia determined by PSG. We utilized the phase-amplitude coupling (PAC) method to analyze the modulation index (MI) trends between different frequency bands during NREM (N1/N2/N3), REM, and wakefulness stages in OSA patients with varying severity levels. We also examined the correlation between the MI index and OSA hypoxia indices. Apart from reduced N2 sleep duration and increased microarousal index, the sleep architecture remained largely unchanged among OSA patients with varying severity levels. Compared to the mild OSA group, patients with moderate and severe OSA exhibited higher MI values of PAC in the low-frequency theta phase and high-frequency beta amplitude in the frontal and occipital regions during N1 sleep and wakefulness. No significant differences in the MI of phase-amplitude coupling were observed during N2/3 and REM sleep. Moreover, the MI of phase-amplitude coupling in theta and beta bands positively correlated with hypoxia-related indices, including the apnea-hypopnea index (AHI) and oxygenation desaturation index (ODI), and the percentage of oxygen saturation below 90% (SaO2<90%). OSA patients demonstrated increased MI values of theta phase and beta amplitude in the frontal and occipital regions during N1 sleep and wakefulness. This suggests that cortical coupling is prevalent and exhibits sleep-stage-specific patterns in OSA. Theta-beta PAC during N1 and wakefulness was positively correlated with hypoxia-related indices, suggesting a potential relationship between these neural oscillations and OSA severity. The present study provides new insights into the relationship between neural oscillations and respiratory hypoxia in OSA patients.

Memory’s gatekeeper: The role of PFC in the encoding of congruent events

Theoretical models conventionally portray the consolidation of memories as a slow process that unfolds during sleep. According to the classical Complementary Learning Systems theory, the hippocampus (HPC) rapidly changes its connectivity during wakefulness to encode ongoing events and create memory ensembles that are later transferred to the prefrontal cortex (PFC) during sleep. However, recent experimental studies challenge this notion by showing that new information consistent with prior knowledge can be rapidly consolidated in PFC during wakefulness and that PFC lesions disrupt the encoding of congruent events in the HPC. The contributions of the PFC to memory encoding have therefore largely been overlooked. Moreover, most theoretical frameworks assume random and uncorrelated patterns representing memories, disregarding the correlations between our experiences. To address these shortcomings, we developed a HPC-PFC network model that simulates interactions between the HPC and PFC during the encoding of a memory (awake stage), and subsequent consolidation (sleeping stage) to examine the contributions of each region to the consolidation of novel and congruent memories. Our results show that the PFC network uses stored memory "schemas" consolidated during previous experiences to identify inputs that evoke congruent patterns of activity, quickly integrate it into its network, and gate which components are encoded in the HPC. More specifically, the PFC uses GABAergic long-range projections to inhibit HPC neurons representing input components correlated with a previously stored memory "schema," eliciting sparse hippocampal activity during exposure to congruent events, as it has been experimentally observed.

Vortioxetine’s impact on the autonomic nervous system in depressed children and adolescents: analysis of the heart rate variability

Relationship between depressive disorder and autonomic nervous system has been already discussed. Reduced emotional regulation is supposed to be associated with prefrontal hypofunction and subcortical hyperactivity. The aim of this study was to determine the effect of vortioxetine on heart rate variability (HRV), a parameter of cardiac autonomic regulation, in depressed hospitalized paediatric patients and assess the clinical effectiveness of the drug in this population. We performed repeated polysomnography analyses at admission and after a short treatment in hospital (15.2 days on average) and measured various HRV parameters (RRi, pNN50, RMSSD, LF-HRV, HF-HRV) during wakefulness, N3 and REM sleep stages. Out of 27 study subjects, 67% have improved depression symptoms as well as anxiety and subjective sleep quality after short vortioxetine treatment. We have found a significant decrease in parasympathetic parameters pNN50, RMSSD and HF-HRV during N3 sleep phase, though not exclusively among vortioxetine responders. The anticipated increase in cardiovagal regulation after vortioxetine treatment was not demonstrated in this pilot study, possibly due to the drug’s multimodal mechanism and impact on the nucleus tractus solitarii, particularly its antagonism on 5HT-3 receptors. Application of selective drugs could further explain the effect of vortioxetine on HRV in depressed patients.

The impact of cannabis use proximal to sleep and cannabinoid metabolites on sleep architecture.

Cannabis is a common sleep aid, however the effects of its use prior to sleep are poorly understood. This study aims to determine the impact of non-medical whole plant cannabis use three hours prior to sleep and measured cannabis metabolites on polysomnogram measures. This is a cross-sectional study of 177 healthy adults who provided detailed cannabis use history, underwent a one-night home sleep test (HST) and had measurement of eleven plasma and urinary cannabinoids, quantified using mass spectroscopy, the morning after the HST. Multivariable models were used to assess the relationship between cannabis use proximal to sleep, which was defined as use three hours before sleep, and individual HST measurements. Correlation between metabolite concentrations and polysomnogram measures were assessed. In adjusted models, cannabis use proximal to sleep was associated with increased wake after sleep onset (median 60.5 versus 45.8 minutes), rate ratio 1.59 (1.22, 2.05), and increased proportion of stage one sleep (median 15.2% versus 12.3%), effect estimate 0.16 (0.06, 0.25). Compared to non-users, frequent cannabis users (>20 days per month) also had increased wake after sleep onset and stage one sleep, in addition to increased REM latency and decreased percent sleep efficiency. Delta-9 tetrahydrocannabinol metabolites correlated with these HST measures. Cannabis use proximal to sleep was associated with minimal changes in sleep architecture. Its use wasn't associated with measures of improved sleep including increased sleep time or efficiency and may be associated with poor quality sleep through increase wake onset and stage 1 sleep.

Predominant cardiac sympathetic modulation during wake and sleep in patients with Rett syndrome

BackgroundRett syndrome (RTT) is a rare neurological disorder primarily associated with mutations in the methyl-CpG-binding protein 2 (MECP2) gene. The syndrome is characterized by cognitive, social, and physical impairments, as well as sleep disorders and epilepsy. Notably, dysfunction of the autonomic nervous system is a key feature of the syndrome. Although Heart Rate Variability (HRV) has been used to investigate autonomic nervous system dysfunction in RTT during wakefulness, there is still a significant lack of information regarding the same during sleep. Therefore, our aim was to investigate cardiovascular autonomic modulation during sleep in subjects with RTT compared to an age-matched healthy control group (HC). MethodA complete overnight polysomnographic (PSG) recording was obtained from 11 patients with Rett syndrome (all females, 10 ± 4 years old) and 11 HC (all females, 11 ± 4 years old; p = 0.48). Electrocardiogram and breathing data were extracted from PSG and divided into wake, non-REM, and REM sleep stages. Cardiac autonomic control was assessed using symbolic non-linear heart rate variability analysis. The symbolic analysis identified three patterns: 0 V% (sympathetic), 2UV%, and 2LV% (vagal). ResultsThe 0 V% was higher in the RTT group than in the HC group during wake, non-REM, and REM stages (p < 0.01), while the 2LV and 2UV% were lower during wake and sleep stages (p < 0.01). However, the 0 V% increased similarly from the wake to the REM stage in both RTT and HC groups. ConclusionsTherefore, the sympatho-vagal balance shifted towards sympathetic predominance and vagal withdrawal during wake and sleep in RTT, although cardiac autonomic dynamics were preserved during sleep.

Wavelet Entropy Analysis of Electroencephalogram Signals During Wake and Different Sleep Stages in Patients with Insomnia Disorder.

To investigate the changes in the wavelet entropy during wake and different sleep stages in patients with insomnia disorder. Sixteen patients with insomnia disorder and sixteen normal controls were enrolled. They underwent scale assessment and two consecutive nights of polysomnography (PSG). Wavelet entropy analysis of electroencephalogram (EEG) signals recorded from all participants in the two groups was performed. The changes in the integral wavelet entropy (En) and individual-scale wavelet entropy (En(a)) during wake and different sleep stages in the two groups were observed, and the differences between the two groups were compared. The insomnia disorder group exhibited lower En during the wake stage, and higher En during the N3 stage compared with the normal control group (all P < 0.001). In terms of En(a), patients with insomnia disorder exhibited lower En(a) in the β and α frequency bands during the wake stage compared with normal controls (β band, P < 0.01; α band, P < 0.001), whereas they showed higher En(a) in the β and α frequency bands during the N3 stage than normal controls (β band, P < 0.001; α band, P < 0.001). Wavelet entropy can reflect the changes in the complexity of EEG signals during wake and different sleep stages in patients with insomnia disorder, which provides a new method and insights about understanding of pathophysiological mechanisms of insomnia disorder. Wavelet entropy provides an objective indicator for assessing sleep quality.

A sleep staging model based on core body temperature rhythm

Core body temperature (CBT) rhythm and sleep process are closely intertwined, yet there is a lack of quantitative methods to link them. To accomplish the research link of “sleeping thermal environment - body temperature - sleep quality” and also to establish a new sleep staging methodology, sleep staging models based on random forest algorithm were constructed. These models fulfilled the requirement of distinguishing four sleep stages (Waking stage, REM stage, Light sleep and Deep sleep) based on the CBT rhythm. The database utilized for model construction emanated from a winter sleep experiment, wherein seven male and seven female participants underwent a consecutive three-night testing protocol within laboratory settings. Data analysis results confirmed that (1) For the separately constructed sleep staging models for males and females, their accuracies stood at 0.70 and 0.77, respectively. The overall performance of the female model marginally surpassed that of the male model; (2) The predominant error in existing models arisen from misjudgments of adjacent sleep stages, such as misclassifying REM stage and deep sleep as light sleep, and this phenomenon may arise from the individual difference in CBT distribution; (3) Personalized sleep staging models have promising prospects, with their peak accuracy reaching an impressive 95% - nearly approaching the performance of gold standard polysomnography (PSG). In summary, this study elucidates the inherent quantitative connection between CBT rhythm and sleep stages, and the proposed sleep staging model has practical value.

Automatic Wake and Deep-Sleep Stage Classification Based on Wigner-Ville Distribution Using a Single Electroencephalogram Signal.

This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner-Ville Distribution (WVD) for time-frequency analysis to determine EEG energy per second in specific frequency bands (δ, θ, α, and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician's visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study's methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10-20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future.

Comparison of power spectra from overnight electroencephalography between patients with Down syndrome and matched control subjects.

Electroencephalograms can capture brain oscillatory activities during sleep as a form of electrophysiological signals. We analysed electroencephalogram recordings from full-night in-laboratory polysomnography from 100 patients with Down syndrome, and 100 age- and sex-matched controls. The ages of patients with Down syndrome spanned 1 month to 31 years (median 4.4 years); 84 were younger than 12 years, and 54 were male. From each electroencephalogram, we extracted relative power in six frequency bands or rhythms (delta, theta, alpha, slow sigma, fast sigma, and beta) from six channels (frontal F3 and F4, central C3 and C4, and occipital O1 and O2) during five sleep stages (N3, N2, N1, R and W)-180 features in all. We examined differences in relative power between Down syndrome and control electroencephalograms for each feature separately. During wake and N1 sleep stages, alpha rhythms (8.0-10.5 Hz) had significantly lower power in patients with Down syndrome than controls. Moreover, the rate of increase in alpha power with age during rapid eye movement sleep was significantly slower in Down syndrome than control subjects. During wake and N1 sleep, delta rhythms (0.25-4.5 Hz) had higher power in patients with Down syndrome than controls. During N2 sleep, slow sigma rhythms (10.5-12.5 Hz) had lower power in patients with DS than controls. These findings extend previous research from routine electroencephalogram studies demonstrating that patients with Down syndrome had reduced circadian amplitude-the difference between wake alpha power and deep sleep delta power was smaller in Down syndrome than control subjects. We envision that these brain oscillatory activities may be used as surrogate markers for clinical trials for patients with Down syndrome.

High stability of EEG spectral power across polysomnography and multiple sleep latency tests in good sleepers and chronic insomniacs

To assess the stability of electroencephalographic (EEG) spectral features across overnight polysomnography (PSG) and daytime multiple sleep latency tests (MSLTs) in chronic insomniacs (CIs) and normal controls (NCs). A total of 20 NCs and 22 CIs underwent standard PSG and MSLTs. Spectral analyses were performed on EEG data from PSG and MSLTs and absolute and relative power in central, frontal and occipital channels were obtained for wake (W) and non-rapid eye movement sleep stage 1 and 2 (N1, N2). Intraclass correlation coefficients (ICCs) were used to assess the stability of EEG spectral power across PSG and MSLTs for W, N1 and N2. The absolute power of all frequency bands except delta exhibited high stability across PSG and MSLTs in both NCs and CIs (ICCs ranged from 0.430 to 0.978). Although delta absolute power was stable in NCs during N1 and N2 stages (ICCs ranged from 0.571 to 0.835), it tended to be less stable in CIs during W and sleep stages (ICCs ranged from 0.042 to 0.807). We also observed lower stability of relative power compared to absolute power though the majority of relative power outcomes maintained high stability in both groups (ICCs in relative power ranged from 0.044 to 0.962). Most EEG spectral bandwidths across PSG and MSLT in W, N1 and N2 show high stability in good sleepers and chronic insomniacs. EEG signals from either an overnight PSG or a daytime MSLT may be useful for reliably exploring EEG spectral features during wakefulness or sleep.

Multi-centre arousal scoring agreement in the Sleep Revolution.

We investigated arousal scoring agreement within full-night polysomnography in a multi-centre setting. Ten expert scorers from seven centres annotated 50 polysomnograms using the American Academy of Sleep Medicine guidelines. The agreement between arousal indexes (ArIs) was investigated using intraclass correlation coefficients (ICCs). Moreover, kappa statistics were used to evaluate the second-by-second agreement in whole recordings and in different sleep stages. Finally, arousal clusters, that is, periods with overlapping arousals by multiple scorers, were extracted. The overall similarity of the ArIs was fair (ICC = 0.41), varying from poor to excellent between the scorer pairs (ICC = 0.04-0.88). The ArI similarity was better in respiratory (ICC = 0.65) compared with spontaneous (ICC = 0.23) arousals. The overall second-by-second agreement was fair (Fleiss' kappa = 0.40), varying from poor to substantial depending on the scorer pair (Cohen's kappa = 0.07-0.68). Fleiss' kappa increased from light to deep sleep (0.45, 0.45, and 0.53 for stages N1, N2, and N3, respectively), was moderate in the rapid eye movement stage (0.48), and the lowest in the wake stage (0.25). Over a half of the arousal clusters were scored by one or two scorers, and less than a third by at least five scorers. In conclusion, the scoring agreement varied depending on the arousal type, sleep stage, and scorer pair, but was overall relatively low. The most uncertain areas were related to spontaneous arousals and arousals scored in the wake stage. These results indicate that manual arousal scoring is generally not reliable, and that changes are needed in the assessment of sleep fragmentation for clinical and research purposes.

Changes In Objective Characteristics In Brain Electrical Activity In Newborns As A Function Of Birth Weight

The aim of the present study was to detect characteristic features of oscillatory electrical activity of the brain in the first day of postnatal life depending on the weight of newborns. Material — Eighteen neonates of conditionally normal gestational age (37.7±1.5 weeks) weighing 2500±720 g were included in the study. All neonates were children of first births of mothers aged 18-35 years, all pregnancies were physiologic, conventionally normal, without significant complications. The height of the newborns was 47±4.643 cm and head circumference was33.0±2.908 cm. The Apgar score at delivery was 7-9 points. All newborns were divided among groups 1 (weight: 2850-4000 gr), 2 (weight: 2000-2800 gr) and 3 (1200-2000 gr). Each newborn underwent EEG monitoring (EEG, monopolar recording, channels C3 and C4) for 40 minutes during the first 12 hours after birth. Methods — Automatic processing of EEG was performed without separating the monitoring records into sleep and wakefulness stages. Oscillatory patterns were calculated for each EEG channel based on the continuous wavelet transform method. Statistical estimations of the number and duration of oscillatory patterns developing in different EEG frequency ranges were performed. Results — A strong correlation was found between neonatal birth weight and integral characteristics of the number \ duration of oscillatory patterns in the low-frequency band [4; 6] Hz (r=-0.878\0.920). Practically healthy newborns with different birth weights show statistically different EEG characteristics in the [4; 6] Hz band in the first 12 hours after birth (p-value≤0.005). Conclusion — Electrical activity of the brain varies significantly depending on the weight of newborns immediately after birth. Monitoring of EEG signals according to the proposed algorithm may become the basis for the development of additional tools for early detection of possible disorders of neurological development of the newborn.

Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep.

Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under-explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High-density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre- and post-auditory stimulation. Results showed that the complexity and the spectral slope in the 2-20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20-40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre-stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness.

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.

Functional Interactions of Low‐density Electroencephalography During Sleep Cycles to Detect Alzheimer’s Disease

AbstractBackgroundAlzheimer’s disease (AD) causes disruption in the collective dynamics of the brain activities which can be captured by analyzing the functional interactions between electroencephalographic (EEG) signals in different regions of the brain. To better assess the AD‐related changes in the interactions, one possible approach is to increase the EEG electrode density for better spatial information. Alternatively, high order interactions within the fluctuations of low‐density EEG — a concept of the multivariate information theory — may better reveal complex EEG interactions and can be used to detect AD‐related changes in brain activities. This study is aimed to demonstrate this conceptual approach using machine learning.MethodWe analyzed overnight 4‐channel EEG recordings (left central, right central, left mastoid, and right mastoid) in the polysomnography (PSG) data collected from 28 older men in the Osteoporotic Fractures in Men Study (MrOS). 14 participants reported a history of clinical diagnosis of AD while the other 14 did not. Groups were matched in age and education. Each of 30‐second epochs of the PSG recording was scored as wake, N1, N2, N3, or rapid eye movement sleep according to the established guideline. Features based on low (2) and high order (3‐4) interactions between electrodes, i.e., total correlation (TC), dual total correlation (DTC), O‐information (O) and S‐information (S), were extracted from raw and band‐passed filtered data (δ: 0.5‐4 Hz, θ: 4‐8 Hz, α: 8‐12 Hz, β: 12‐30 Hz, γl: 30‐40 Hz, γh: 40‐100 Hz) within each wake/sleep stage. The optimal set of features was identified using a cross‐validated random forest classifier to maximize the group discrimination.ResultAD was discriminated from control with an average area under the receiver operating characteristic curve of 0.90±0.05 using just 3 features: O (triplet) measured in the γh band within wake stage, DTC (triplet) of raw EEG within N1 stage, and TC (pairwise) in the δ band within N1 stage.ConclusionThis proof‐of‐concept study shows the possibility of using low‐density sleep EEG to detect AD. This method might be useful for screening people with risk of AD in ambulatory setting.

Nocturnal Scratching and Quality of Sleep in Children with Atopic Dermatitis.

Itching due to atopic dermatitis causes sleep disorders in children, but its pathology is unknown. The aim of this study is to investigate nocturnal scratching as an indirect index of itching during sleep and its relationship with depth of sleep in children with atopic dermatitis. Nocturnal scratching was measured in a total of 20 children with atopic dermatitis, using a smartwatch installed with the application Itch Tracker. Depth of sleep was analysed using polysomnography. The severity of atopic dermatitis was scored using Eczema Area and Severity Index (EASI) and Patient-Oriented Eczema Measure (POEM). The number and time of nocturnal scratching measured by Itch Tracker had a significantly positive correlation with EASI scores, whereas POEM scores were not correlated with EASI scores. Mean sleep efficiency was 90.0% and scratching episodes (n = 67) started mainly during the awake stage or light sleep stages. In the scratching episodes that started during sleep stages (n = 34), the sleep stage changed to a lighter one or to the awake stage in 35.5% of episodes. Itch Tracker is applicable to measure nocturnal scratching in children. Nocturnal scratching can deteriorate quality of sleep by changing the sleep stage to a lighter one or to the awake stage.