Sleep Stage Changes Research Articles

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-hour 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.

Single-Channel Sleep EEG Classification Method Based on LSTM and Hidden Markov Model

Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term memory and a hidden Markov model (LSTM-HMM). First, the single-channel EEG is decomposed using wavelet transform (WT), and multi-domain features are extracted from the component signals to characterize the EEG characteristics fully. Considering the temporal nature of sleep stage changes, this paper uses a multi-step time series as the input for the model. After that, the multi-step time series features are input into the LSTM. Finally, the HMM improves the classification results, and the final prediction results are obtained. Results: A complete experiment was conducted on the Sleep-EDFx dataset. The results show that the proposed method can extract deep information from EEG and make full use of the sleep stage transition rule. The proposed method shows the best performance in single-channel sleep EEG classification; the accuracy, macro average F1 score, and kappa are 82.71%, 0.75, and 0.76, respectively. Conclusions: The proposed method can realize single-channel sleep EEG classification and provide a reference for other EEG classifications.

Evaluation of Autonomic Nervous System Function During Sleep by Mindful Breathing Using a Tablet Device: Randomized Controlled Trial.

One issue to be considered in universities is the need for interventions to improve sleep quality and educational systems for university students. However, sleep problems remain unresolved. As a clinical practice technique, a mindfulness-based stress reduction method can help students develop mindfulness skills to cope with stress, self-healing skills, and sleep. We aim to verify the effectiveness of mindful breathing exercises using a tablet device. In total, 18 nursing students, aged 18-22 years, were randomly assigned and divided equally into mindfulness (Mi) and nonmindfulness (nMi) implementation groups using tablet devices. During the 9-day experimental period, cardiac potentials were measured on days 1, 5, and 9. In each sleep stage (sleep with sympathetic nerve dominance, shallow sleep with parasympathetic nerve dominance, and deep sleep with parasympathetic nerve dominance), low frequency (LF) value, high frequency (HF) value, and LF/HF ratios obtained from the cardiac potentials were evaluated. On day 5, a significant correlation was observed between sleep duration and each sleep stage in both groups. In comparison to each experimental day, the LF and LF/HF ratios of the Mi group were significantly higher on day 1 than on days 5 and 10. LF and HF values in the nMi group were significantly higher on day 1 than on day 5. The correlation between sleep duration and each sleep stage on day 5 suggested that sleep homeostasis in both groups was activated on day 5, resulting in similar changes in sleep stages. During the experimental period, the cardiac potentials in the nMi group showed a wide range of fluctuations, whereas the LF values and LF/HF ratio in the Mi group showed a decreasing trend over time. This finding suggests that implementing mindful breathing exercises using a tablet device may suppress sympathetic activity during sleep. UMIN-CTR Clinical Trials Registry UMIN000054639; https://tinyurl.com/mu2vdrks.

Associations between longitudinal changes in sleep stages and risk of cognitive decline in older men.

To investigate the relationships between longitudinal changes in sleep stages and the risk of cognitive decline in older men. This study included 978 community-dwelling older men who participated in the first (2003-2005) and second (2009-2012) sleep ancillary study visits of the Osteoporotic Fractures in Men Study. We examined the longitudinal changes in sleep stages at the initial and follow-up visits, and the association with concurrent clinically relevant cognitive decline during the 6.5-year follow-up. Men with low to moderate (quartile 2, Q2) and moderate increase (Q3) in N1 sleep percentage had a reduced risk of cognitive decline on the modified mini-mental state examination compared to those with a substantial increase (Q4) in N1 sleep percentage. Additionally, men who experienced a low to moderate (Q2) increase in N1 sleep percentage had a lower risk of cognitive decline on the Trails B compared with men in the reference group (Q4). Furthermore, men with the most pronounced reduction (Q1) in N2 sleep percentage had a significantly higher risk of cognitive decline on the Trails B compared to those in the reference group (Q4). No significant association was found between changes in N3 and rapid eye movement sleep and the risk of cognitive decline. Our results suggested that a relatively lower increase in N1 sleep showed a reduced risk of cognitive decline. However, a pronounced decrease in N2 sleep was associated with concurrent cognitive decline. These findings may help identify older men at risk of clinically relevant cognitive decline.

Large muscle group movements during sleep in restless leg syndrome: neurophysiological and clinical implications.

Recently, criteria have been drawn up for large muscle group movements during sleep (LMM), defined as movements lasting for 3-45 seconds in adults, which are often accompanied by changes in sleep stage, arousals, and increases in heart rate. The aim of this study was to characterize LMM in restless legs syndrome (RLS) in order to better evaluate their impact on the neurophysiology of the disorder and, therefore, the possible clinical implications. Consecutive, drug-free patients diagnosed with RLS and controls, aged 18 years or more, were retrospectively enrolled. Leg movement activity-short-interval (SILMS), periodic (PLMS), and isolated (ISOLMS) leg movements during sleep-and LMM were detected and scored. In total, 100 patients and 67 controls were recruited. All movement measures were significantly higher in RLS. A significant positive correlation was found between LMM and ISOLMS index but not PLMS index in both groups. LMM index showed a significant negative correlation with total sleep time, sleep efficiency, and percentage of sleep stages N3 and R, as well as a significant positive correlation with the number of awakenings, and percentage of sleep stages N1 and N2 only in patients with RLS. No significant correlation was found between either LMM or PLMS index and RLS severity. Different types of movements, including SILMS, ISOLMS, and LMM, play somewhat distinct roles in sleep neurophysiology in RLS. Notably, LMM, a newly recognized category of movements, demonstrates associations with sleep architecture instability and fragmentation, arousals, and awakenings, suggesting potential clinical implications.

Nightmares and Sleep Disturbances in Children with PTSD: A Polysomnographic and Actigraphy Approach Evaluation.

Sleep disturbances (insomnia and nightmare symptoms) are the most sensitive and persistent symptoms of pediatric post-traumatic stress disorder (PTSD). Untreated, these sleep disturbances (SD) associated with PTSD are predictive of PTSD persistence and increased psychiatric complications. The aim of this study was to evaluate sleep and circadian rhythms in children with PTSD under both laboratory and ecological conditions in comparison with a control population and to test for the first time the hypothesis that SD and circadian rhythms are positively correlated with PTSD severity and its comorbidities. This prospective pilot study evaluated PTSD, SD (insomnia, nightmares), and sleep-wake rhythms in 11 children with PTSD (aged 3-18), compared with the age and sex-matched control groups. Assessment of PTSD and subjective and objective measures of sleep and sleep-wake rhythms (questionnaires, 24-h in-laboratory video-polysomnography, 15-day at-home actigraphy recording) were performed between 1 and 6 months after the traumatic event. Children with PTSD had higher sleep fragmentation (increased wake-after-sleep onset, increased number of sleep stage changes) compared to controls, with a change in sleep microarchitecture (micro-arousal index at 14.8 versus 8.2, p = 0.039). Sleep fragmentation parameters correlated with PTSD symptomatology, insomnia, and post-traumatic nightmare severity. The within-group comparison revealed a better sleep architecture in the controlled (sleep laboratory) than in the ecological condition (at home) (total sleep time 586 versus 464 min, p = 0.018). Sleep and rhythm disturbances are strongly associated with PTSD in children. The assessment of SD in children with PTSD should be carried out systematically and preferentially under ecological conditions, and management of SD should integrate the environment (environmental design, psycho-education for the children and their parents) more fully into therapy focused on sleep and trauma.

Change of Sleep Stage during Gastroesophageal Reflux in Infants.

This study intended to explore the existence of a temporal association of changes of sleep stage and gastroesophageal reflux (GER) in infants. Documentation of sleep stage and GER was conducted via the use of synchronized polygraphic recording combined with impedance-pH-metry in 15 infants. The total recording-time (Rt) was divided into GER-"window-time" (five seconds before and after the onset of a GER episode), "remaining GER time", and "GER-free time", and analyzed for changes of sleep stage. a total of 462 GER episodes were identified during Rt (151.1 h) in all infants. During 1.3 h of window-time; 61 changes of sleep stage (47/h); during 5.9 h of Remaining GER-time, 139 changes of sleep stage (24/h); and during 143.9 h of GER-free time, 4087 changes of sleep stage (28/h) were documented. Change of sleep stage was strongly associated with the onset of GER (p < 0.02 and p < 0.05, respectively). There is a strong temporal association between sleep irregularities, i.e., changes of sleep and episodes of GER in infants. When dealing with disturbed sleep in infants, GER should be considered by caregivers.

Detecting change points in neural population activity with contrastive metric learning.

Finding points in time where the distribution of neural responses changes (change points) is an important step in many neural data analysis pipelines. However, in complex and free behaviors, where we see different types of shifts occurring at different rates, it can be difficult to use existing methods for change point (CP) detection because they can't necessarily handle different types of changes that may occur in the underlying neural distribution. Additionally, response changes are often sparse in high dimensional neural recordings, which can make existing methods detect spurious changes. In this work, we introduce a new approach for finding changes in neural population states across diverse activities and arousal states occurring in free behavior. Our model follows a contrastive learning approach: we learn a metric for CP detection based on maximizing the Sinkhorn divergences of neuron firing rates across two sides of a labeled CP. We apply this method to a 12-hour neural recording of a freely behaving mouse to detect changes in sleep stages and behavior. We show that when we learn a metric, we can better detect change points and also yield insights into which neurons and sub-groups are important for detecting certain types of switches that occur in the brain.

Sleep patterns correlates with the efficacy of tDCS on post-stroke patients with prolonged disorders of consciousness

BackgroundThe subclassification of prolonged disorders of consciousness (DoC) based on sleep patterns is important for the evaluation and treatment of the disease. This study evaluates the correlation between polysomnographic patterns and the efficacy of transcranial direct current stimulation (tDCS) in patients with prolonged DoC due to stroke.MethodsIn total, 33 patients in the vegetative state (VS) with sleep cycles or without sleep cycles were randomly assigned to either active or sham tDCS groups. Polysomnography was used to monitor sleep changes before and after intervention. Additionally, clinical scale scores and electroencephalogram (EEG) analysis were performed before and after intervention to evaluate the efficacy of tDCS on the patients subclassified according to their sleep patterns.ResultsThe results suggest that tDCS improved the sleep structure, significantly prolonged total sleep time (TST) (95%CI: 14.387–283.527, P = 0.013) and NREM sleep stage 2 (95%CI: 3.157–246.165, P = 0.040) of the VS patients with sleep cycles. It also significantly enhanced brain function of patients with sleep cycles, which were reflected by the increased clinical scores (95%CI: 0.340–3.440, P < 0.001), the EEG powers and functional connectivity in the brain and the 6-month prognosis. Moreover, the changes in NREM sleep stage 2 had a significant positive correlation with each index of the β band.ConclusionThis study reveals the importance of sleep patterns in the prognosis and treatment of prolonged DoC and provides new evidence for the efficacy of tDCS in post-stroke patients with VS patients subclassified by sleep pattern.Trial registration URL: https://www.clinicaltrials.gov. Unique identifier: NCT03809936. Registered 18 January 2019

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&amp;lt;.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).

214 Longitudinal Changes in Sleep Duration, Timing, Variability, and Stages during the COVID-19 Pandemic: Large-Scale Fitbit Data

IntroductionThe COVID-19 pandemic has resulted in societal-level changes to sleep and other behavioral patterns. Objective, longitudinal data would allow for a greater understanding of sleep-related changes at the population level.MethodsN= 163,524 deidentified active Fitbit users from 6 major US cities contributed data, representing areas particularly hard-hit by the pandemic (Chicago, Houston, Los Angeles, New York, San Francisco, and Miami). Sleep variables extracted include nightly and weekly mean sleep duration and bedtime, variability (standard deviation) of sleep duration and bedtime, and estimated arousals and sleep stages. Deviation from similar timeframes in 2019 were examined. All analyses were performed in Python.ResultsThese data detail how sleep duration and timing changed longitudinally, stratified by age group and gender, relative to previous years’ data. Overall, 2020 represented a significant departure for all age groups and both men and women (P<0.00001). Mean sleep duration increased in nearly all groups (P<0.00001) by 5-11 minutes, compared to a mean decrease of 5-8 minutes seen over the same period in 2019. Categorically, sleep duration increased for some and decreased for others, but more extended than restricted. Sleep phase shifted later for nearly all groups (p<0.00001). Categorically, bedtime was delayed for some and advanced for others, though more delayed than advanced. Duration and bedtime variability decreased, owing largely to decreased weekday-weekend differences. WASO increased, REM% increased, and Deep% decreased. Additional analyses show stratified, longitudinal changes to sleep duration and timing mean and variability distributions by month, as well as effect sizes and correlations to other outcomes.ConclusionThe pandemic was associated with increased sleep duration on average, in contrast to 2019 when sleep decreased. The increase was most profound among younger adults, especially women. The youngest adults also experienced the greatest bedtime delay, in line with extensive school-start-times and chronotype data. When given the opportunity, the difference between weekdays and weekends became smaller, with occupational implications. Sleep staging data showed that slightly extending sleep minimally impacted deep sleep but resulted in a proportional increase in REM. Wakefulness during the night also increased, suggesting increased arousal despite greater sleep duration.Support (if any)This research was supported by Fitbit, Inc.

Scoring of large muscle group movements during sleep: an International Restless Legs Syndrome Study Group position statement.

There is a gap in the manuals for scoring sleep-related movements because of the absence of rules for scoring large movements. A taskforce of the International Restless Legs Syndrome Study Group (IRLSSG) elaborated rules that define the detection and quantification of movements involving large muscle groups. Consensus on each of the criteria in this article was reached by testing the presence of consensus on a first proposal; if no consensus was achieved, the concerns were considered and used to modify the proposal. This process was iterated until consensus was reached. A preliminary analysis of the duration of movements involving large muscle groups was also carried out on data from two previous studies, which, however, used a visual analysis of video-polysomnographic (PSG) recordings obtained from children or adults. Technical specifications and scoring rules were designed for the detection and quantification of large muscle group movements during sleep with a duration between 3 and 45 seconds in adults or 3 and 30 seconds in children, characterized by an increase in electromyographic activity and/or the occurrence of movement artifact in any combination of at least two recommended channels and not meeting the criteria for any other type of movement. Large muscle group movements are often accompanied by sleep stage changes, arousals, awakenings, and heart rate rises. The absence of clear and detailed rules defining them has likely impeded the development of studies that might disclose their clinical relevance; these new rules fill this gap.

Analysis of the characteristics of sleep in patients with patent foramen ovale complicated with obstructive sleep apnea.

Analyzing sleep quality and sleep structure in patients with patent foramen ovale (PFO) complicated with obstructive sleep apnea (OSA) and the interaction between OSA and PFO in sleep. We compared patients with PFO complicated with OSA, patients with simple PFO, and controls. Pittsburgh Sleep Quality Index was used to compare sleep quality and polysomnography was used to compare sleep structure of the three groups. Compared with the control group(n = 62), PFO with OSA (n = 48) and simple PFO (n = 61) groups had more frequent occurrence of poor sleep quality (χ2 = 89.901; p < 0.001). These two groups also showed decreased sleep efficiency (p < 0.010),lower percentages of REM sleep, and reducedN3 sleep (p < 0.050). The N2 sleep was prolonged (p < 0.010). The nocturnal lowest SpO2 was lower and the oxygen desaturation index was higher (p < 0.50). Compared with the simple PFO group, the poor sleep quality was more frequent in the PFO with OSA group; sleep latency (p < 0.001) was prolonged; wake after sleep onset (p < 0.001) and arousal times (p = 0.031) were increased; and sleep micro-arousal index (p = 0.037), periodic leg movement index (p = 0.024), and apnea hypopnea index (p < 0.001) were higher in the PFO with OSA group. Patients withPFO and OSA have poor sleep quality with changes in sleep stage and high occurrence rate of sleep disorders. OSA further deteriorates sleep quality and alters sleep structure in patients with PFO.

The effect of altering routine husbandry factors on sleep duration and memory consolidation in the horse

Sleep is a critically important behaviour for all mammals due to its fundamental role within homeostatic/circadian systems and memory consolidation. As a large and vigilant prey species that is highly sensitive to stimuli at night, the horse sleeps less than other mammalian species. For this reason, the domestic environment has the potential to greatly affect the duration and quality of equine sleep. This study aimed to determine the effect of environmental factors on equine sleep stages, and whether this would influence cognitive performance during a spatial memory task. Ten riding school horses (mixed breed/ height/ sex; average age 14.9 + 2.4 years) were randomly assigned to two groups (n = 5) within a five-week crossover repeated measures design experiment. Each group experienced a combination of one of two light conditions (lights on = Treatment; lights off = Control), and one of two bedding depth treatments (15 cm bed = control; 5 cm bed = treatment) for six days. Duration of sleep stage behaviours (standing Non-Rapid Eye Movement [NREM]), sternal NREM, sternal Rapid Eye Movement [REM] and lateral REM) were measured continuously using CCTV infrared cameras. For the spatial memory task, latency, number of correct responses, and differences between these parameters during training and testing days were measured. A repeated measures general linear model assessed the effects of treatment conditions on duration of sleep stage, and changes in sleep stage over time (bedding and light set as within-subject factors). Wilcoxon Signed-Rank and paired t-tests determined differences in memory task parameters between treatments. Comparing Treatment Bedding with Control Bedding conditions, horses spent on average significantly less time in lateral REM (0.34 ± 0.12 versus 0.46 ± 0.13 h; p = 0.032) and sternal NREM (0.64 ± 0.10 versus 0.80 ± 0.12 h; p = 0.007), and significantly more time in standing NREM (3.69 ± 0.76 versus 3.17 ± 0.77; p = 0.024). Only sternal REM was significantly affected during the Treatment Light condition compared to control conditions (0.53 ± 0.07 versus 0.67 ± 0.11; p = 0.031). Interactions between day and treatment were apparent for specific sleep stage behaviours indicative of acclimatisation. No significant effects (p > 0.05) of Treatment Light or Bedding conditions were detected for performance during the spatial memory test. Overall, horses exposed to sub-optimal conditions tended to display significantly less time in recumbent sleep stages (NREM and REM) and increased time in a standing NREM stage. The impact of reduced sleep on equine cognition requires further study.

Cortical source localization of sleep-stage specific oscillatory activity

The oscillatory features of non-REM sleep states have been a subject of intense research over many decades. However, a systematic spatial characterization of the spectral features of cortical activity in each sleep state is not available yet. Here, we used magnetoencephalography (MEG) and electroencephalography (EEG) recordings during night sleep. We performed source reconstruction based on the individual subject’s anatomical magnetic resonance imaging (MRI) scans and spectral analysis on each non-REM sleep epoch in eight standard frequency bands, spanning the complete spectrum, and computed cortical source reconstructions of the spectral contrasts between each sleep state in comparison to the resting wakefulness. Despite not distinguishing periods of high and low activity within each sleep stage, our results provide new information about relative overall spectral changes in the non-REM sleep stages.

Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models

Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topological changes in different sleep stages. However, the number of network links can quickly grow above the number of parameters that are typically analyzed with standard statistical methods. Artificial Neural Networks (ANNs) are a promising approach as they are successful in large parameter spaces, such as in digital imaging. On the other hand, ANN models do not provide an intrinsic approach to interpret their predictions, and they typically require large training data sets. Both aspects are critical in biomedical research. Medical decisions need to be explainable, and large data sets of quality assured patient and control data are rare. In this paper, different models for the classification of insomnia-a common sleep disorder-have been trained with 59 patients and age and gender matched controls, based on their physiological networks. Feature relevance evaluation is employed for all methods. For ANNs, the extrinsic interpretation method DeepLift is applied. The results are not identical across methods, but certain network links have been rated as relevant by all or most of the models. While ANNs show less classification accuracy (0.89) than advanced tree-based models (0.92 and 0.93), DeepLift provides an in-depth ANN interpretation with feature relevance scores for individual data samples. The analysis revealed modifications in the pulmonar, ocular, and cerebral subnetworks that have not been described before but are consistent with known findings on the physiological impact of insomnia.

The Inspection on Obstructive Sleep Apnea Severity Detection using a Deep Learning Access

As of late, crucial endeavors are created to research thoroughgoing sleep observant to anticipate sleeprelated clutters. variable sleep organize grouping has attained unbelievable enthusiasm among specialists in well-being information science. A soft induction framework is received to assess the division of sleep organize. At that time, a starter sleep profundity is decided. Besides, a restricted state machine is made to tell apart the sleep stage changes. the excellence between our examination and alternative existing investigations is that, first, each the load sensors and also the pulse device area unit utilized; at that time, the soft induction and a restricted state machine area unit bestowed, that provide United States of America the next truth than the standard techniques to assess the sleep prepare. preventive sleep disorder (OSA) may be a typical sleep issue caused by abnormal reposeful. The seriousness of OSA will prompt various aspect effects, as an example, fulminant viscus death (SCD). Polysomnography (PSG) may be a very best quality level for OSA analysis. It records various sign from the patient's body for in any event one entire night and figures the ApneaHypopnea Index (AHI) that is that the amount of symptom or respiration occurrences each hour. This value is then accustomed prepare patients into OSA seriousness levels. The principle focal points of our projected technique incorporate easier data acquisition, prompt OSA seriousness recognition, and undefeated part extraction while not space learning from ability. Programmed sleep-organize arrangement models were worked with sturdy and explainable AI techniques (support vector machine and call tree).

Better sleep quality could reinforce the relationship between sleep posture and kidney stone laterality in obstructive sleep apnea patients.

Urolithiasis is a common urologic problem among adults worldwide. It is interesting that urinary stones tend to recur on the same side and the pathophysiology of unilateral stone formation is not clearly understood. Researchers found that sleep posture could alter renal perfusion, and subsequent vascular injury may lead to urolithiasis formation. The aim of this study is to retrospectively evaluate the correlations between the specific sleep postures recorded in polysomnography (PSG) and the stone laterality in unilateral urinary stone formers with obstructive sleep apnea (OSA). We retrospectively reviewed patients receiving PSG for the diagnosis of OSA in our institutes between January 1st 2002 and September 30th 2016. Those had a history of unilateral urinary stones which surgical intervention was warranted were included. Supine, prone, right decubitus, and left decubitus sleep postures were determined as patients remained specific postures for more than 50% of their sleep time. The laterality of stones with sleep posture was analyzed with Chi-square test. A student t test was used to assess factors that influenced the ipsilateral stone formation. IBM SPSS Statistics 21 software was adopted to analyze the data, and p value less than 0.05 was considered statistically significant. 117 patients were enrolled in this study, among which 105 (89.7%) were male and 12 were female. The mean age at PSG examination was 53.5 ± 11.5years. 66 patients were diagnosed as left-side urolithiasis, and 51 patients had urinary stones at right side. 58 (49.6%) patients slept predominantly in supine posture, while 53 (45.3%) patients, in decubitus posture, and no patient, in prone posture. A total of 51 patients' stones appeared at the same side as their decubitus posture (p < 0.0001). Shorter sleep stage I ratio (24.3 ± 15.5% versus 52.4 ± 24.7%, p = 0.017), longer sleep stage II + III + IV ratio (66.2 ± 21.9% versus 30.9 ± 2.8, p = 0.049), and less sleep stage change (176.6 ± 69.6 times versus 282.5 ± 91.2 times, p = 0.041) were correlated with stone formation on the same side. Age, gender, body mass index, and other polysomnographic parameters failed to demonstrate associations between the sides of urinary calculi and sleep postures. We discovered that sleep posture is associated with unilateral urolithiasis formation. Favorable sleep quality and less sleep stage changes could consolidate the correlations. Further prospective study is warranted to evaluate the effect of sleep posture and quality on non-OSA patients as well as the preventive effect of sleep posture modification on repeated unilateral stone formers.

Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population.

Objective: Recent research suggests that sleep disorders or changes in sleep stages or EEG waveform precede over time the onset of the clinical signs of pathological cognitive impairment (e.g., Alzheimer's disease). The aim of this study was to identify biomarkers based on EEG power values and spindle characteristics during sleep that occur in the early stages of mild cognitive impairment (MCI) in older adults.Methods: This study was a case-control cross-sectional study with 1-year follow-up of cases. Patients with isolated subjective cognitive complaints (SCC) or MCI were recruited in the Bordeaux Memory Clinic (MEMENTO cohort). Cognitively normal controls were recruited. All participants were recorded with two successive polysomnography 1 year apart. Delta, theta, and sigma absolute spectral power and spindle characteristics (frequency, density, and amplitude) were analyzed from purified EEG during NREM and REM sleep periods during the entire second night.Results: Twenty-nine patients (8 males, age = 71 ± 7 years) and 29 controls were recruited at T0. Logistic regression analyses demonstrated that age-related cognitive impairment were associated with a reduced delta power (odds ratio (OR) 0.072, P < 0.05), theta power (OR 0.018, P < 0.01), sigma power (OR 0.033, P < 0.05), and spindle maximal amplitude (OR 0.002, P < 0.05) during NREM sleep. Variables were adjusted on age, gender, body mass index, educational level, and medication use. Seventeen patients were evaluated at 1-year follow-up. Correlations showed that changes in self-reported sleep complaints, sleep consolidation, and spindle characteristics (spectral power, maximal amplitude, duration, and frequency) were associated with cognitive impairment (P < 0.05).Conclusion: A reduction in slow-wave, theta and sigma activities, and a modification in spindle characteristics during NREM sleep are associated very early with a greater risk of the occurrence of cognitive impairment. Poor sleep consolidation, lower amplitude, and faster frequency of spindles may be early sleep biomarkers of worsening cognitive decline in older adults.

A Non-Invasive Sleep Analysis Approach Based on a Fuzzy Inference System and a Finite State Machine

This paper deals with the analysis of sleep quality, which involves a non-invasive sleep stage detection method with home deployability. Some physiological signals, such as heart rate, heart rate variation, and the number of times the subject rolled over, are collected to determine the sleep stage. A fuzzy inference system is adopted to evaluate the division of sleep stage. Then, a preliminary sleep depth is calculated. Furthermore, a finite-state machine is developed to detect the sleep stage changes. The difference between our research and other existing studies is that, first, both the pressure sensors and the heart rate device are employed; then, the fuzzy inference and a finite-state machine are introduced, which give us a higher precision than the traditional methods to evaluate the sleep stage. The experimental results show that the proposed method can well evaluate the sleep quality that is almost consistent with a polysomnography test. The latter is currently recognized as the best way to measure sleep quality, which, however, requires a variety of monitoring sensors and has to be performed by the nursing staff in a professional setting. The reported approach can be used for monitoring sleep quality or sleep disorder screening at home.