Light Sleep Stages Research Articles

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.

Breathing cessation events that compose the apnea–hypopnea index are distinctively associated with the adverse outcomes in Alzheimer’s disease

BackgroundPrevious studies challenge the impact of obstructive sleep apnea (OSA) once patients are diagnosed with Alzheimer’s disease (AD). Nevertheless, OSA recognizably disrupts sleep, and relevant associations between sleep, AD pathological markers, and cognition have been demonstrated. We aimed to further explore this, evaluating the associations between each breathing cessation event that compose the apnea–hypopnea index (AHI) and the sleep structure to finally investigate whether this was related to increased levels of AD markers and higher cognitive decline.MethodsObservational, prospective study, including consecutive patients diagnosed with mild-moderate AD. The participants were submitted to overnight polysomnography followed by a cerebrospinal fluid collection for AD pathological markers levels determination. Neuropsychological assessment was performed at baseline and after 12 months of follow-up.ResultsThe cohort was composed of 116 patients (55.2% females) with a median [p25;p75] age of 76.0 [72.0;80.0] years and an AHI of 25.9 [15.1;48.5], which was mainly defined by the presence of hypopneas and obstructive apneas. These were distinctively associated with the sleep structure, with obstructive apneas being related to arousals and sleep lightening and hypopneas being related to an increased number of arousals only. Despite having a lower frequency, mixed and central apneas also presented associations with the sleep structure, particularly increasing the time spent in the lighter sleep stages. In relation to AD pathological markers, obstructive and mixed apneas were related to an augment in neurofilament light levels while hypopneas were associated with a higher phosphorylated-tau/amyloid-beta protein ratio. Hypopneas were the most important event for an increased cognitive decline at the 12-month follow-up.ConclusionsOur findings highlight the importance of a patient-centered approach, with a comprehensive and detailed analysis of the AHI to effectively predict the different outcomes and tailor the appropriate therapeutic strategies.

0845 Individuals with Persistent Low Back Pain Have Less Time in Deep Sleep Stage than Asymptomatic Controls

Abstract Introduction Sleep consists of stages of light sleep, deep sleep, and rapid eye movement (REM), which have different characteristics. These stages are thought to be associated with different physiological and psychological processes. Pain is an unpleasant sensory and emotional experience generated by neural activity that potentially impacts several networks in the brain. There is limited information regarding the potential association between objectively determined sleep characteristics and persistent low back pain (pLBP). The purpose of this study was to examine whether sleep characteristics differ between individuals with pLBP and asymptomatic controls. Methods Individuals in a current episode of back pain with symptoms impacting function and persisting greater than 3 months (pLBP group, n=20) and asymptomatic individuals (control group, n=16) participated in the study. They wore a home sleep test device (Zmachine® Insight+, General Sleep Corporation) for three nights. Results The pLBP and control groups had similar age (24.8 ± 5.8 and 25.4 ± 4.3 years, respectively; mean ± SD), height, weight, and waist and hip circumferences. The two groups also had similar total sleep time (6.0 ± 1.2 and 6.6 ± 1.0 hours, for pLBP and control groups, respectively), sleep efficiency (82.8 ± 8.8% and 84.9 ± 5.7%), and wake time after sleep onset (35.8 ±27.4 and 31.5 ±18.1 minutes). The duration in REM (1.5 ± 0.5 and 1.5 ± 0.6 hours) and light sleep (3.1 ± 0.7 and 3.4 ± 0.9 hours) stages were also similar. However, the pLBP group had less deep sleep time (1.4 ± 0.4 hours) than the control group (1.7 ± 0.3 hours) (p = 0.029). Conclusion Individuals with pLBP had less deep sleep time than asymptomatic controls, although other objectively determined sleep characteristics appear to be similar. These results suggest an association between the deep sleep stage and persistent low back pain that requires further investigation. Support (if any) National Institutes of Health R01HD095959 and University of South Carolina ASPIRE 80004373.

Sleep architecture as a candidate for phenotyping sleep bruxism: A narrative physiological review.

Sleep bruxism (SB), an oral behaviour in otherwise healthy individuals, is characterised by frequent rhythmic masticatory muscle activity (RMMA) during sleep. RMMA/SB episodes occur over various sleep stages (N1-N3 and rapid eye movement (REM)), sleep cycles (non-REM to REM), and frequently with microarousals. It currently remains unclear whether these characteristics of sleep architecture are phenotype candidates for the genesis of RMMA/SB. This narrative review investigated the relationship between sleep architecture and the occurrence of RMMA as a SB phenotype candidate. PubMed research was performed using keywords related to RMMA/SB and sleep architecture. In non-SB and SB healthy individuals, RMMA episodes were most frequent in the light non-REM sleep stages N1 and N2, particularly during the ascending phase of sleep cycles. The onset of RMMA/SB episodes in healthy individuals was preceded by a physiological arousal sequence of autonomic cardiovascular to cortical activation. It was not possible to extract a consistent sleep architecture pattern in the presence of sleep comorbidities. The lack of standardisation and variability between subject complexified the search for specific sleep architecture phenotype(s). In otherwise healthy individuals, the genesis of RMMA/SB episodes is largely affected by oscillations in the sleep stage and cycle as well as the occurrence of microarousal. Furthermore, a specific sleep architecture pattern cannot be confirmed in the presence of sleep comorbidity. Further studies are needed to delineate sleep architecture phenotype candidate(s) that contribute to the more accurate diagnosis of SB and treatment approaches using standardised and innovative methodologies.

Effect of Emotional Distress on Sleep Physiology in Underserved Pregnant Women [ID: 1381497

INTRODUCTION: Pregnancy is characterized by an altered pattern of emotions and sleep. The aim of this study was to examine the effect of emotional distress on objective sleep parameters in underserved pregnant women during the COVID-19 pandemic. METHODS: Institutional review board approval was obtained for the study. This was a longitudinal observation study in which we administered weekly validated self-reported surveys (Patient Health Questionnaire-2, Generalized Anxiety Disorder-2, COVID-19-related anxiety, and life-related stressors) to consented pregnant women over the course of their second and third trimesters (n=13). The independent variable, subjective emotional distress, was derived from a sum score of these weekly surveys. A wearable device was used to measure objective sleep physiological data, such as the rapid eye movement (REM), deep, and light sleep stages. The dependent variables were obtained from weekly average scores of the sleep data. Multilevel analysis was conducted, controlling for relevant covariates. RESULTS: Adjusting for gestational age, maternal age at enrollment, and prepregnancy body mass index, higher emotional distress was associated with a shorter duration of deep sleep (b=−.65, P<.05) and longer duration of REM sleep (b=.79, P<.01). There was no significant relationship between emotional distress and light sleep. CONCLUSION: Our study appears to be the first to provide preliminary evidence that emotional distress negatively affects sleep in terms of decreased deep sleep and increased REM sleep during pregnancy. Findings suggest that further research is needed to understand the role of sleep in the relationships between emotional distress and adverse maternal and infant health outcomes.

A Smart Flexible Vital Signs and Sleep Monitoring Belt Based on MEMS Triaxial Accelerometer and Pressure Sensor

People spend about one third of their lifetime in sleep, and sleep quality has a great impact on people’s health. Therefore, vital signs and sleep quality monitoring are more and more important. In this article, a novel smart flexible sleep monitoring belt with MEMS triaxial accelerometer and pressure sensor is developed to detect vital signs, snore events, and sleep stages. Besides, the related algorithms and methods for data preprocessing, heart and respiration rates detection, snoring recognition, and sleep stages classification are proposed in detail. Then, a series of sleep experiments is performed based on the experimental platform for the smart flexible belt, and the test results measured by PolySomnoGraphy are used as the golden standards for comparison. The experimental results demonstrate that the detection accuracies of heart rate and respiration rate of the belt are about 1.5 and 0.7 bpm, respectively. The accuracy of 97.2% is achieved by the proposed snoring recognition method. In addition, as for 2-stage analysis, the sensitivities of awake and asleep stages are 90.2% and 100%, respectively; meanwhile, the corresponding accuracy of sleep stages prediction is as high as 95.1%. However, as for 4-stage analysis, the sensitivities of awake, rapid eye movement, light sleep, and deep sleep stages are 90.2%, 77.1%, 78.1%, and 73.5%, respectively; meanwhile, the corresponding accuracy of sleep stages prediction decreases to 79.7%. In general, the test results indicate that vital signs detection, snoring recognition, and sleep stages classification based on the sleep monitoring belt are feasible and effective. Hence, this smart flexible belt can be widely used for sleep monitoring at home due to low cost and high performance.

Verification methodology for Smart Awakening Systems.

A mental and physical recovery after an awakening moment depends not only on the overall sleep duration and quality but mostly on the sleep stage in the waking moment. The most comfortable awakening moment is during the Light or Wake sleep stages. But the fix-time alarm clock doesn't take into account the sleep stage in the awakening moment, which often results in awakening during the Deep or Rapid Eyes Movement stages. To reduce the negative recovery effect, big companies and research groups develop various awakening systems. Such systems recognize sleep stages based on wearable sensors' data (mostly from accelerometer sensors) and thus can find the easiest awakening moment time with minimal recovery effects.However, it is quite hard to measure and verify the efficiency of such systems without using polysomnography (which can be performed only in clinical conditions by medical experts). To solve this problem we developed a methodology based on questionnaires and psychological tests. Such an approach has big scalability, does not require special medical equipment, and can be evaluated in a home environment with minimal support effort. The proposed verification approach has been tested on smartwatches with the sleep stages forecast model. The proposed model accuracy was 78%. Results of our experiment show that the majority of users demonstrated a correlation between awakening quality and the verification tests performance.

Polysomnographic markers of suicidal ideation in untreated unipolar major depressed individuals

Given the major role played by sleep in the particular relationship between suicidality and major depression, the aim of this study was to empirically identify polysomnographic markers specific to suicidal ideation in major depressed individuals in order to allow better suicide prevention in this high-risk subpopulation. Demographic and polysomnographic data from 190 individuals (34 healthy controls and 156 untreated unipolar major depressed individuals) recruited from the sleep laboratory database were analysed. Suicidal ideation were considered present if the score in item G of the Beck Depression Inventory was ≥1 and/or if they were highlighted during the systematic psychiatric assessment conducted on admission to the sleep laboratory. Independently of depression severity, major depressed individuals with suicidal ideation present a decrease in deep NREM sleep (slow-wave sleep) and an increase in light NREM sleep (stage 1 + stage 2) compared to those without suicidal ideation. There are no significant differences for the other polysomnographic parameters. In our study, we highlighted the existence of potential polysomnographic markers of suicidal ideation in untreated unipolar major depressed individuals, which seems to open up new perspectives for the identification and management of individuals at high-risk of suicide in this particular subpopulation.

285 Pilot field study of Ambulatory Sleep-Staging in Shift-Working Air Traffic Controllers

Abstract Introduction The simplicity of wrist actigraphy for sleep-wake monitoring in the field contributes to its ubiquity in shift-work research. However, devices based solely on recording activity levels are generally not suitable to quantify sleep architecture. This is a limitation as quantifying changes in sleep stages caused by circadian misalignment is important to better assess the consequences of sleep-wake disruption in shift-working populations. This pilot study was conducted to evaluate whether sleep stages vary with respect to different shift types. Methods Six male air traffic controllers aged 48.5±8.4 years (mean±SD) completed the protocol which entailed two ~9-day periods, each with up to 6 workdays. Schedules comprised 1 or 2 early night shifts (19:30–03:30h), followed by an evening shift (15:00–23:00h), day shift (09:00–17:00h), morning shift (06:30–14:30h), and 1 or 2 full night shifts (23:00–7:00h). A portable sleep-staging device (Somno-Art, Paris, France) that monitored activity levels and heart rate was worn on the non-dominant forearm during bedtime and produced estimates of REM and NREM sleep stages with a proprietary algorithm. Total sleep time (TST) and sleep stages were assessed per shift type with mixed-effects models. Results Final analyses were based on 70 sleep periods preceding workdays, standardized to 24 h to account for the different intervals between consecutive shifts. Analyses revealed significant effects of shift type for TST (p=.016), stages N1 (p=.010) and N2 (p=.043), but none for N3 (p=.055) or REM (p=.117) sleep. TST and stage N1 sleep prior to night shifts was shorter than for day, evening, or early night shifts (all p<.05). Participants obtained less stage N2 sleep prior to night shifts than days shifts (p=.049). Conclusion This pilot study suggests variations in TST across shifts were predominantly due to differences in light sleep stages, whereas no significant differences in N3 and REM sleep were observed. Thus, while TST was reduced for night shifts, participants obtained similar durations of the most recuperative stages. These findings highlight the importance of refined monitoring of sleep in field research involving shift-work. Support (if any) Project funded by NAV Canada. Devices lent by the Somno-Art company. A.K. received a postdoctoral fellowship from the Fonds de Recherche en Santé du Québec (FRQS).

Cyclic alternating pattern in obstructive sleep apnea: A preliminary study.

Obstructive sleep apnea is linked to cardiovascular disease, metabolic disorders and dementia. The precise nature of the association between respiratory events in obstructive sleep apnea, cortical or subcortical arousals, and cognitive, autonomic and oxidative stress consequences remains incompletely elucidated. Previous studies have aimed to understand the relationship between obstructive sleep apnea and arousal patterns, as defined by the cyclic alternating pattern, but results have been inconsistent, in part likely due to the presence of associated comorbidities. To better define this relationship, we analysed cyclic alternating patterns in patients with obstructive sleep apnea without any additional comorbidities. We identified 18 adult male, non-obese subjects with obstructive sleep apnea and no other comorbidities or medication history, who underwent whole-night electroencephalography and polysomnography. Cyclic alternating pattern analysis was performed and verified by certified somnologists. Pairwise linear regression analysis demonstrated an inverse relationship between obstructive sleep apnea severity and cyclic alternating pattern subtype A1, and a direct correlation with cyclic alternating pattern subtype A3. Cyclic alternating pattern subtypes A1 prevail in milder obstructive sleep apnea phenotype, whilst cyclic alternating pattern subtypes A2 and A3 overcome among moderate-to-severe obstructive sleep apnea patients. The milder obstructive sleep apnea group also presented higher sleep efficiency, and increased percentages of non-rapid eye movement stage 3 and rapid eye movement sleep, as well as longer cyclic alternating pattern sequences in N3, while severe obstructive sleep apnea patients spent more time in lighter sleep stages. These results imply/suggest a balance between cyclic alternating pattern's adaptive and maladaptive arousal processes in obstructive sleep apnea of differing severities. In milder obstructive sleep apnea (apnea-hypopnea index <20), sleep continuity may be reinforced by cyclic alternating pattern subtype A1, whereas in more severe obstructive sleep apnea, decompensation of these sleep-stabilizing mechanisms may occur and more intrusive cyclic alternating pattern fluctuations disrupt sleep circuitry.

Cerebral functional networks during sleep in young and older individuals

Even though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals

Sleep is a fundamental human physiological activity required for adequate working of the human body. Sleep disorders such as sleep movement disorders, nocturnal front lobe epilepsy, insomnia, and narcolepsy are caused due to low sleep quality. Insomnia is one such sleep disorder where a person has difficulty in getting quality sleep. There is no definitive test to identify insomnia; hence it is essential to develop an automated system to identify it accurately. A few automated methods have been proposed to identify insomnia using either polysomnogram (PSG) or electroencephalogram (EEG) signals. To the best of our knowledge, we are the first to automatically detect insomnia using only electrocardiogram (ECG) signals without combining them with any other physiological signals.In the proposed study, an optimal antisymmetric biorthogonal wavelet filter bank (ABWFB) has been used, which is designed to minimize the joint duration-bandwidth localization (JDBL) of the underlying filters. The L1-norm feature is computed from the various wavelet sub-bands coefficients of ECG signals. The L1 norm features are fed to various supervised machine learning classifiers for the automated detection of insomnia. In this work, ECG recordings of seven insomnia patients and six normal subjects are used from the publicly available cyclic alternating pattern (CAP) sleep database.We created ten different subsets of ECG signals based on annotations of sleep-stages, namely wake (W), S1, S2, S3, S4, rapid eye moment (REM), light sleep stage (LSS), slow-wave sleep (SWS), non-rapid eye movement (NREM) and W + S1+S2+S3+S4+REM for the automated identification of insomnia. Our proposed ECG-based system obtained the highest classification accuracy of 97.87%, F1-score of 97.39%, and Cohen's kappa value of 0.9559 for K-nearest neighbour (KNN) with the ten-fold cross-validation strategy using ECG signals corresponding to the REM sleep stage. The support vector machine (SVM) yielded the highest value of 0.99 for area under the curve with the ten fold cross-validation corresponding to REM sleep stage.

Home-Use and Real-Time Sleep-Staging System Based on Eye Masks and Mobile Devices with a Deep Learning Model

PurposeSleep is an important human activity. Comfortable sensing and accurate analysis in sleep monitoring is beneficial to many healthcare and medical applications. From 2020, owing to the COVID‑19 pandemic that spreads between people when they come into close physical contact with one another, the willingness to go to hospital for receiving care has reduced; care-at-home is the trend in modern healthcare. Therefore, a home-use and real-time sleep-staging system is developed in this paper.MethodsWe developed and implemented a real-time sleep staging system that integrates a wearable eye mask for high-quality electroencephalogram/electrooculogram measurement and a mobile device with MobileNETV2 deep learning model for sleep-stage identification. In the experiments, 25 all-night recordings were acquired, 17 of which were used for training, and the remaining eight were used for testing.ResultsThe averaged scoring agreements for the wake, light sleep, deep sleep, and rapid eye movement stages were 85.20%, 87.17%, 82.87%, and 89.30%, respectively, for our system compared with the manual scoring of PSG recordings. In addition, the mean absolute errors of four objective sleep measurements, including sleep efficiency, total sleep time, sleep onset time, and wake after sleep onset time were 1.68%, 7.56 min, 5.50 min, and 3.94 min, respectively. No significant differences were observed between the proposed system and manual PSG scoring in terms of the percentage of each stage and the objective sleep measurements.ConclusionThese experimental results demonstrate that our system provides high scoring agreements in sleep staging and unbiased sleep measurements owing to the use of EEG and EOG signals and powerful mobile computing based on deep learning networks. These results also suggest that our system is applicable for home-use real-time sleep monitoring.

EFFECTS OF TRANSVENOUS PHRENIC NERVE STIMULATION ON CENTRAL SLEEP APNEA AND SLEEP ARCHITECTURE: THE 5 YEAR ANALYSIS

EFFECTS OF TRANSVENOUS PHRENIC NERVE STIMULATION ON CENTRAL SLEEP APNEA AND SLEEP ARCHITECTURE: THE 5 YEAR ANALYSIS

U-133, a Chaperone Inducer, Eliminates Sleep Disturbances in a Model of the Preclinical Stage of Parkinson’s Disease in Aged Rats

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disease that is closely associated with aging. It is considered incurable due to its late diagnosis and the use of symptomatic treatment, which cannot alter the molecular mechanisms of sleep disruption nor the neurodegenerative processes that develop with aging and PD progression. The present study assesses the therapeutic potential of a novel chaperone inducer, U-133 (acetyl 2,3,7-tris-O-glucoside echinochrome), in the preclinical stage of PD modelled in aged rats via inhibition of the proteasomal system in the brain. U-133 is a derivative of the sea-urchin pigment echinochrome (2,3,5,7,8-pentahydroxy-1,4-naphthoquinone). It is produced by glycosylation, which possesses neuroprotective, antioxidant, and anticancer properties. The administration of U-133, which induces the synthesis of Hsp 70i and HSP 40 heat-shock proteins in the brain, precludes an increase in the light-sleep (drowsiness) stage and a decrease in the total time of deep, slow-wave sleep, both of which occur with the progression of the preclinical stage of PD modelled in aged Wistar rats. Deep, slow-wave sleep is thought to promote glymphatic clearance and to accelerate protein synthesis. Thus, the U-133-induced increase in the proportion of deep, slow-wave sleep as compared to the preclinical model is considered to have a neuroprotective effect that contributes to the intensification of the restorative function of neurons and counteracts the progression of neurodegeneration.

A deep learning approach to detect sleep stages

Abstract 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%.

Automatic Identification of Insomnia Based on Single-Channel EEG Labelled With Sleep Stage Annotations

Monitoring single-channel EEG is a promising home-based approach for insomnia identification. Currently, many automatic sleep stage scoring approaches based on single-channel EEG have been developed, whereas few studies research on automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations. In this paper, we propose a one-dimensional convolutional neural network (1D-CNN) model for automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations, and further investigate the identification performance based on different sleep stages EEG epochs. Single-channel EEG on 9 insomnia patients and 9 healthy subjects was used in this study. We constructed 4 subdatasets from EEG epochs based on the sleep stage annotations: All sleep stage dataset (ALL-DS), REM sleep stage dataset (REM-DS), light sleep stage dataset (LSS-DS), and SWS sleep stage dataset (SWS-DS). Subsequently, 4 subdatasets were fed into our 1D-CNN. We conducted experiments under intra-patient and inter-patient paradigms, respectively. Our experiments demonstrated that our 1D-CNN leveraging 3 subdatasets composed of REM, LSS and SWS epochs, respectively, achieved higher average accuracies in comparison with baseline methods under both intra-patient and inter-patient paradigms. The experimental results also indicated that amongst all the sleep stages, 1D-CNN leveraging REM and SWS epochs exhibited the best insomnia identification average accuracies in intra-patient paradigm, which are 98.98% and 99.16% respectively, whereas no statistically significant difference was found in inter-patient paradigm. For automatic insomnia identification based on single-channel EEG labelled with sleep stage annotations, 1D-CNN model introduced in this paper could achieve superior performance than traditional methods.

A sleep intervention study comparing effects of sleep restriction and fragmentation on sleep and vigilance and the need for recovery

PurposeSleep deprivation is present not only in sleep disorders but also in numerous high demanding jobs and negatively affects cognition, performance and health. We developed a study design to distinguish the effects and need for recovery of two short-term disturbances – intermittent sleep fragmentation and partial sleep restriction. MethodsThe randomized within-subjects design contained two weeks each with a baseline night, an intervention night of either sleep deprivation (5 h) or sleep fragmentation (light on every hour) and two undisturbed recovery nights. Twenty healthy male participants (mean age: 39.9 ± 7.4 years, mean BMI: 25.5 ± 2.2 kg/m²) underwent polysomnography, a psychomotor vigilance task (PVT), and subjective questions on well-being and sleep efficiency. ResultsPercentage-wise, the restriction night had significant less wake times, less light sleep (stage 1), less REM sleep, but more deep sleep (stage 3) than the fragmentation night. The restriction week displayed a significant recovery effect regarding these sleep stages. The sleep fragmentation week presented a significant recovery effect regarding sleep onset times. PVT performance showed only a slight recovery effect after sleep restriction. Subjective sleep quality was reduced after both interventions with a significant recovery effect during restriction week only. ConclusionsShort-term sleep restriction presented as a stronger sleep disturbance than short-term intermittent sleep fragmentation, including a stronger need for recovery. Already a one night sleep deprivation had an effect beyond two recovery days. The PVT was not sensitive enough to reveal significant changes. Next, autonomic parameters as possible biomarkers will be investigated.

Sleep Is Compromised in -12° Head Down Tilt Position.

Recent studies are elucidating the interrelation between sleep, cranial perfusion, and cerebrospinal fluid (CSF) circulation. Head down tilt (HDT) as a simulation of microgravity reduces cranial perfusion. Therefore, our aim was to assess whether HDT is affecting sleep (clinicaltrials.gov; identifier NCT 02976168). 11 male subjects were recruited for a cross-over designed study. Each subject participated in two campaigns each comprising 3 days and 2 nights. Intervention started on the second campaign day and consisted of maintenance of horizontal position or −12° HDT for 21 h. Ultrasound measurements were performed before, at the beginning and the end of intervention. Polysomnographic measurements were assessed in the second night which was either spent in horizontal posture or at −12° HDT. Endpoints were sleep efficiency, sleep onset latency, number of sleep state changes and arousals, percentages of N3, REM, light sleep stages and subjective sleep parameters. N3 and REM sleep reduced by 25.6 and 19.1 min, respectively (P = 0.002, g = -0.898; P = 0.035, g = -0.634) during −12° HDT. Light sleep (N1/2) increased by 33.0 min at −12° HDT (P = 0.002, g = 1.078). On a scale from 1 to 9 subjective sleep quality deteriorated by 1.3 points during −12° HDT (P = 0.047, g = -0.968). Ultrasonic measurement of the venous system showed a significant increase of the minimum (P = 0.009, P < 0.001) and maximum (P = 0.004, P = 0.002) cross-sectional area of the internal jugular vein at −12° HDT. The minimum cross-sectional area of the external jugular vein differed significantly between conditions over time (P = 0.001) whereas frontal skin tissue thickness was not significantly different between conditions (P = 0.077, P = 0.811). Data suggests venous congestion at −12° HDT. Since subjects felt comfortable with lying in −12° HDT under our experimental conditions, this posture only moderately deteriorates sleep. Obviously, the human body can almost compensate the several fold effects of gravity in HDT posture like an affected CSF circulation, airway obstruction, unusual patterns of propioception and effects on the cardiovascular system.

Automatic Sleep Stage Classification using Weighted ELM and PSO on Imbalanced Data from Single Lead ECG

Sleep stage classification is one of important aspects in sleep studies, which can give clinical information for diagnosing sleep disorder and measuring sleep quality. Due to the difference in sleep stage proportion for every person, the collected sleep stage data are imbalanced naturally, which can lead to high probability of misclassification. Various learning method has been developed to classify sleep stage based on electrocardiogram (ECG) signal. However, to the best of our knowledge, there are no researches which consider the imbalanced dataset problem for sleep stage classification. In this research, a classification model of sleep stage based on ECG signal was developed using Weighted Extreme Machine Learning (WELM) to deal with imbalanced learning dataset and Particle Swarm Optimization (PSO) for feature selection. The research will use the MIT-BIH Polysomnographic Database, which contains 10154 sleep stage annotated ECG data which consist of 17.79%, 38.28%, 4.76%, 1.78%, 6.89%, and 30.5% data of NREM1, NREM2, NREM3, NREM4, REM, and awake stage respectively. From each ECG record, a total of 18 features were extracted and the feature selection process resulted in 10 features which highly affect the sleep stage classification. The proposed model successfully obtained a mean accuracy of 78,78% for REM, NREM and Wake stage classification and 73.09% for Light Sleep, Deep Sleep, REM, and Wake stage classification.