Sleep Stages Research Articles

The effect of central sleep apnea on sleep architecture in children with obstructive sleep apnea

ObjectiveThis study aimed to investigate how central sleep apnea (CSA) impacts sleep patterns in children with obstructive sleep apnea (OSA). MethodsChildren undergoing polysomnography (PSG) were enrolled and sorted into two groups: those with OSA alone (Group A) and those with both OSA and CSA (CAI <1 nd: children with 10 % CSA or more and less than 50 %, Group B). Statistical analysis was conducted to compare sleep structure and clinical features between Group A and Group B. ResultsGroup B exhibited significantly higher respiratory events, apnea hypoventilation index, apnea index and oxygen desaturation index (ODI) compared to Group A (p < 0.05). Group B also showed higher total sleep time and arousal index than Group A (P < 0.05). The proportion of time spent in stage N3 was lower in Group B than in Group A (P < 0.05). Moreover, mean heart rate and minimum heart rate were higher in Group B compared to Group A (P < 0.05).Minimum oxygenation levels (including non-rapid eye movement (NREM) stages) were lowe in Group B than in Group A (P < 0.05). Additionally, the prevalence of positional obstructive sleep apnea (P-OSA) was greater in Group B than in Group A (P < 0.05). ConclusionIn comparison to those with OSA alone, children with OSA and concurrent CSA exhibited distinct sleep patterns, including reduced N3uration, higher arousal index, longer respiratory events, higher ODI, and lower oxygen saturation, higher heart rate.

Safinamide effect on sleep architecture of motor fluctuating Parkinson's disease patients: A polysomnographic rasagiline-controlled study

IntroductionSleep problems commonly occur in Parkinson's disease (PD) and significantly affect patients' quality of life. A possible effect on subjective sleep disturbances of monoamine oxidase-B inhibitors (MAOB-Is) has been described. MethodsThis prospective, observational, single-centre study involved 45 fluctuating PD patients complaining sleep problems as documented by the PD Sleep Scale -2nd version (PDSS-2 ≥18) starting rasagiline 1 mg/daily or safinamide 100 mg/daily, according to common clinical practice, and maintaining antiparkinsonian therapy unchanged. Polysomnography (PSG), sleep questionnaires (PDSS-2, Epworth Sleepiness Scale - ESS), and motor function were evaluated at baseline (T0) and after 4 months of treatment (T1). ResultsSafinamide was prescribed in thirty patients and rasagiline in fifteen patients. Both drugs induced a significant improvement in Movement Disorder Society Unified PD Rating Scale III scores. Patients treated with rasagiline showed a significant increase in stage 1 (N1) Non-REM sleep compared to T0, with no significant effects on sleep scales. Patients treated with safinamide showed a significant increase in stage 3 of Non-REM sleep and sleep efficiency and a reduction in the rate of periodic limb movements, matching a significant reduction in PDSS-2 and ESS scales compared to T0. ConclusionThis study showed that safinamide, in addition to having a significant effect on PD motor symptoms, like the other MAOB-Is, may exert a specific beneficial effect on subjective and objective sleep, probably driven by its dual mechanism of action, which involves both dopaminergic and glutamatergic neurotransmission.

A Deep Transfer Learning Approach for Sleep Stage Classification and Sleep Apnea Detection Using Wrist-Worn Consumer Sleep Technologies.

Obstructive sleep apnea (OSA) is a common, underdiagnosed sleep-related breathing disorder with serious health implications Objective - We propose a deep transfer learning approach for sleep stage classification and sleep apnea (SA) detection using wrist-worn consumer sleep technologies (CST). Methods - Our model is based on a deep convolutional neural network (DNN) utilizing accelerometers and photo-plethysmography signals from nocturnal recordings. The DNN was trained and tested on internal datasets that include raw data from clinical and wrist-worn devices; external validation was performed on a hold-out test dataset containing raw data from a wrist-worn CST. Results - Training on clinical data improves performance significantly, and feature enrichment through a sleep stage stream gives only minor improvements. Raw data input outperforms feature-based input in CST datasets. The system generalizes well but performs slightly worse on wearable device data compared to clinical data. However, it excels in detecting events during REM sleep and is associated with arousal and oxygen desaturation. We found; cases that were significantly underestimated were characterized by fewer of such event associations. Conclusion - This study showcases the potential of using CSTs as alternate screening solution for undiagnosed cases of OSA. Significance - This work is significant for its development of a deep transfer learning approach using wrist-worn consumer sleep technologies, offering comprehensive validation for data utilization, and learning techniques, ultimately improving sleep apnea detection across diverse devices.

Higher Levels of Morning and Daytime Light Exposure Associated with Positive Sleep Indices in Professional Team Sport Athletes.

Light exposure techniques have been recommended to combat sleep issues caused by disruption to circadian regularity in the athletic population, although studies are lacking. A total of 17 professional male Australian Football athletes (age ± SD: 22 ± 3 years) wore a wrist actigraph to measure sleep parameters, and a wearable light sensor to measure melanopic equivalent daylight illuminance (mEDI, in lux) for 14 days. Participants completed three sleep questionnaires at the end of the data collection period and completed well-being surveys 6 times. The Sleep Regularity Index (SRI) for each player was also calculated from actigraphy data. Light exposure data were organised into three different timeframes: morning (wake time + 2 hours), daytime (end of morning to 6 pm), and evening (2 hours leading up to bedtime) for analysis. Repeated measures correlation was conducted for objective sleep measures and mEDI values per timeframe. Pearson's correlation was conducted on subjective sleep measures and well-being measures against mEDI values per timeframe. Higher morning light was associated with significantly (p < 0.001) greater total sleep time (r = 0.31). Higher daytime light exposure was associated with higher subjective sleep quality (r = 0.48, p < 0.05). Higher evening light exposure was associated with higher Athlete Sleep Screening Questionnaire (ASSQ) global scores (r = 0.52, p < 0.05). There were no other significant correlations between light exposure and sleep or well-being measures (p > 0.05). Higher morning and daylight exposure levels were associated with various positive objective and subjective sleep measures in professional team sport athletes, supporting the need for education on optimising light exposure to improve circadian function, sleep, and health.

Associations of Wearable Ring Measured Sleep, Sedentary Time, and Physical Activity With Cardiometabolic Health: A Compositional Data Analysis Approach.

Movement behaviors within the 24-h day, including physical activity (PA), sedentary time, and sleep, are associated with cardiometabolic health. We aimed to determine the association between 24-h movement composition and cardiometabolic health while accounting for sleep efficiency. Altogether, 1134 participants from the Northern Finland Birth Cohort 1986 study, free from prior cardiovascular disease, provided at least 4 days of 24-h activity and sleep efficiency measured with a wearable ring. Participants' body composition was assessed with bioimpedance, blood pressure, and waist circumference were measured, and lipids and glucose were analyzed from a fasting blood sample. Linear regression models for cardiometabolic outcomes were created with 24-h movement composition and covariates, including sleep efficiency and behavioral and socioeconomic factors. Isotemporal time reallocations were used to demonstrate the dose-dependent associations between time use and outcomes. Beneficial associations with the outcomes were detected when sedentary time was reallocated to light PA, moderate-to-vigorous PA (MVPA), or sleep. For example, substituting 30 min of sedentary time with MVPA was associated with 7.2% (95% CI from -9.8% to -4.5%) lower visceral fat area, 4.9% (95% CI from -6.5% to -3.3%) lower body fat percentage, 1.6% (95% CI from -2.3% to -0.9%) smaller waist circumference, and 2.4% (95% CI from 1.2% to 3.5%) higher high-density lipoprotein (HDL) cholesterol after accounting for gender, marital status, education level, employment, smoking, alcohol consumption, and sleep efficiency. Substituting sedentary time with sleep or light PA showed beneficial but smaller differences in adiposity measures and HDL cholesterol. Limiting sedentary time should be encouraged in adulthood.

Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients With Disorder of Consciousness

Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients With Disorder of Consciousness

Depression Detection Using an Automatic Sleep Staging Method With an Interpretable Channel-Temporal Attention Mechanism

Depression Detection Using an Automatic Sleep Staging Method With an Interpretable Channel-Temporal Attention Mechanism

Sleep stages and brain oscillations: An in-depth analysis using quantitative and mathematical models

This study explores the intricate relationship between sleep stages and brain oscillations, utilizing quantitative EEG analysis and mathematical models. We examine the distinct EEG characteristics of light sleep, deep sleep, and REM sleep, focusing on power spectral density (PSD), coherence, and phase synchronization analyses. The Ising model and Kuramoto model provide frameworks for understanding the synchronization dynamics and phase relationships of neuronal populations across sleep stages. Our findings highlight significant variations in oscillatory activity, coherence, and phase synchronization, offering insights into the neural mechanisms underlying sleep regulation. Additionally, we discuss the implications of these findings for sleep disorders such as insomnia, sleep apnea, and narcolepsy, demonstrating how mathematical models can predict therapeutic outcomes and guide targeted interventions. This comprehensive analysis contributes to the understanding of sleep architecture and the development of novel treatments for sleep disorders.

Efficacy of hyperbaric oxygen therapy as an adjunct therapy in the treatment of sleep disorders among patients with Parkinson's disease: a meta-analysis.

To systematically evaluate the efficacy of hyperbaric oxygen therapy (HBOT) as an adjunct therapy for treating sleep disorders in patients with Parkinson's disease (PD). We conducted comprehensive searches in eight databases from inception through September 2023, including PubMed, Cochrane Library, Embase, Web of Science, SinoMed, China National Knowledge Infrastructure (CNKI), China Science and Technology Periodical Database (VIP), and Wanfang Database. The objective was to identify randomized controlled trials (RCTs) evaluating HBOT's effectiveness in alleviating sleep disorder symptoms in PD patients as an adjunct therapy. Literature screening and data extraction were independently executed by the authors. Meta-analyses were performed using Review Manager 5.3 software, and publication bias and sensitivity analyses were assessed using Stata 17.0 software. Seven RCTs involving 461 participants were included. The findings revealed that the addition of HBOT significantly enhanced sleep efficiency (MD = 15.26, 95% CI [10.89, 19.63], p < 0.00001), increased time in bed (MD = 69.65, 95% CI [43.01, 96.30], p < 0.00001), total sleep time (MD = 75.87, 95% CI [25.42, 126.31], p = 0.003), slow-wave sleep (SWS) time (MD = 6.14, 95% CI [3.95, 8.34], p < 0.00001), and rapid eye movement sleep (REM) time (MD = 4.07, 95% CI [2.05, 6.08], p < 0.0001), and reduced awakening frequency (MD = -11.55, 95% CI [-15.42, -7.68], p < 0.00001) and sleep latency (MD = -6.60, 95% CI [-9.43, -3.89], p < 0.00001). Additionally, significant improvements were observed in the Pittsburgh Sleep Quality Index (PSQI) (MD = -2.52, 95% CI [-2.85, -2.18], p < 0.00001), Epworth Sleepiness Scale (ESS) (MD = -2.90, 95% CI [-3.34, -2.47], p < 0.00001), Unified Parkinson's Disease Rating Scale Part III (UPDRS III) (MD = -1.32, 95% CI [-2.16, -0.47], p = 0.002), and Hoehn and Yahr grading (H-Y grading) (MD = -0.15, 95% CI [-0.28, -0.01], p = 0.03). The current meta-analysis supports the efficacy of HBOT as an adjunct therapy in managing sleep disorders in PD patients. It is recommended for PD patients experiencing sleep disturbances.Systematic review registration:https://www.crd.york.ac.uk/, identifier: CRD42023462201.

Prognostic Factors Related to Sleep Quality in Patients With Obstructive Sleep Apnea After Positive Airway Pressure Therapy

Background and Objectives: This study aimed to evaluate the factors that influence deep sleep restoration in patients with obstructive sleep apnea (OSA) following positive airway pressure (PAP) therapy.Methods: In total, 363 patients diagnosed with OSA who received PAP therapy over at least 3 months were enrolled in the study. Polysomnographic parameters, anatomical characteristics, and subjective sleep-related parameters were evaluated according to the presence of daytime sleepiness and morning headache before and after 3 months of PAP treatment.Results: Age was significantly different according to whether excessive daytime sleepiness (EDS) was alleviated (average: 49.35 years) or persisted (average: 52.82 years) (p=0.001). Age was also significantly associated with morning headache (p=0.037). Body mass index (BMI) was higher in the alleviated EDS group (28.70 kg/m&lt;sup&gt;2&lt;/sup&gt;) than in the persistent EDS group (27.13 kg/m&lt;sup&gt;2&lt;/sup&gt;; p=0.002). The apnea-hypopnea index (AHI) was correlated with the EDS outcome (p=0.011). The group with alleviated EDS had a longer mandibular plane to hyoid distance (MPH) than the group with persistent EDS (17.95 mm vs. 15.38 mm; p&lt;0.001). However, BMI, AHI, and MPH showed no significant associations with morning headache. Epworth Sleepiness Scale scores were higher in the alleviated EDS and alleviated morning headache groups (EDS: p&lt;0.001, morning headache: p=0.001). Self-Efficacy Measure for Sleep Apnea (SEMSA) values differed significantly between the EDS groups (p&lt;0.001), but not between the morning headache groups (p=0.122). After 3 months of PAP therapy, the MPH was negatively correlated with EDS in univariate (odds ratio [OR]=0.921, p&lt;0.001) and multivariate analyses (OR=0.937, p=0.028). The SEMSA score was also negatively correlated with EDS in univariate (OR=0.961, p&lt;0.001) and multivariate (OR=0.973, p=0.019) analyses.Conclusion: Age, polysomnographic metrics, and anatomical considerations were important for sleep quality-associated daytime symptoms. In addition, anatomical characteristics and the patient’s self-efficacy were significantly associated with the effect of PAP treatment on sleep quality.

Dexmedetomidine Promotes NREM Sleep by Depressing Oxytocin Neurons in the Paraventricular Nucleus in Mice.

Dexmedetomidine (DEX) is a highly selective α2-adrenoceptor agonist with sedative effects on sleep homeostasis. Oxytocin-expressing (OXT) neurons in the paraventricular nucleus (PVN) of the hypothalamus (PVNOXT) regulate sexual reproduction, drinking, sleep-wakefulness, and other instinctive behaviors. To investigate the effect of DEX on the activity and signal transmission of PVNOXT in regulating the sleep-wakefulness cycle. Here, we employed OXT-cre mice to selectively target and express the designer receptors exclusively activated by designer drugs (DREADD)-based chemogenetic tool hM3D(Gq) in PVNOXT neurons. Combining chemogenetic methods with electroencephalogram (EEG) /electromyogram (EMG) recordings, we found that cannula injection of DEX in PVN significantly increased the duration of non-rapid eye movement (NREM) sleep in mice. Furthermore, the chemogenetic activation of PVNOXT neurons using i.p. injection of clozapine N-oxide (CNO) after cannula injection of DEX to PVN led to a substantial increase in wakefulness. Electrophysiological results showed that DEX decreased the frequency of action potential (AP) and the spontaneous excitatory postsynaptic current (sEPSC) of PVNOXT neurons through α2-adrenoceptors. Therefore, these results identify that DEX promotes sleep and maintains sleep homeostasis by inhibiting PVNOXT neurons through the α2-adrenoceptor.

The nucleus of solitary tract (NTS) synchronizes sleep-wake-state-dependent cortical activity through the parabrachial nucleus (PB) in rat

BackgroundThe medullary nucleus of solitary tract (NTS) and its afferents of vagus nerve have long been investigated in regulation of cortical activity and sleep promotion. However, the underlying neural circuit by which the NTS regulates electroencephalogram (EEG) and sleep remain unclear. As the NTS has a strong projection to the pontine arousal site, the parabrachial nucleus (PB), we proposed the NTS via the pontine parabrachial nucleus (PB) regulates cortical activity and sleep. MethodsWe bilaterally and directly stimulated the NTS neurons by chemogenetic approach and NTS terminals in the PB by optogenetic approach and examined changes in EEG and sleep in rats. ResultsOpto- and chemo-stimulation of the NTS and NTS-PB pathway altered neither sleep amounts nor patterns; however, both stimulations consistently increased EEG delta (0.5–4.0 Hz) EEG power during non-rapid-eye-movement (NREM) sleep and alpha-beta (10–30 Hz) EEG power during wake and REM sleep. ConclusionOur results indicate that the NTS via its projections to the PB synchronizes low frequency EEG during NREM sleep and high frequency EEG during wake and REM sleep. This pathway may serve the neural foundation for the vagus nerve stimulation (VNS) treating cortical disorders.

Assessment of Sleep Stages in Unconscious Patients with Acute Severe Traumatic Brain Injury

Abstract Background The existence of a sleep cycle in deeply unconscious patients with severe traumatic brain injury (TBI) remains unknown. Evaluating the sleep patterns of these patients may provide insights into their consciousness and help predict functional recovery. Aims and Objectives This prospective observational study aimed to assess the usefulness of monitoring the sleep cycle in the prognostication of unconscious TBI patients. Materials and Methods A purposive sampling technique was employed to include 39 patients with severe TBI (Glasgow Coma Scale [GCS] ≤ 8). The average GCS score at admission was (5.76 ± 1.65). Sleep was monitored using an actigraphy smartwatch for 4 consecutive days, (3.15 ± 2.49) days postbrain injury. Sleep cycle monitoring tool and unconscious patient outcome monitoring tool were employed. An outcome assessment was done based on the GCS score. Results Severe TBI patients exhibit intact sleep cycle (i.e., deep sleep, light sleep, and rapid eye movement sleep) despite being deeply unconscious, even in the acute stage. Total sleep duration was found to be significantly higher at a mean of 19.97 hours as compared to the mean of 8 hours as per the smartwatch reference value for normal individuals. Patients with improved sleep cycles had a higher likelihood of GCS improvement (p &lt; 0.05). Conclusion This study is the first of its kind to demonstrate that the sleep cycle is a reliable prognostic factor for the recovery of consciousness in the acute phase of severe TBI among unconscious patients, with improvement in the sleep cycle mirroring neurological improvement.

Habitual rapid eye movementsleep predicts changes in test-anxiety levels weeks in advance.

Previous research has linked rapid eye movement sleep to emotional processing, particularly stress. Lab studies indicate that rapid eye movement sleep deprivation and fragmentation heighten emotional reactivity and stress response. This relationship extends to natural settings, where poor-quality sleep among college students correlates with increased academic stress and lower academic performance. However, there is a lack of research into how specific sleep stages, like rapid eye movement, affect real-life stress development. This study investigated whether habitual rapid eye movement sleep in college students can predict the future development of real-life stress symptoms associated with final exams. Fifty-two participants (mean age = 19 years, 62% females) monitored their sleep for a week during the academic semester using a mobile electroencephalogram device, and then completed self-evaluations measuring test anxiety and other relevant factors. They completed the same evaluations again just prior to final exams. We found that rapid eye movement sleep was the most dominant factor predicting changes in participants' test anxiety. However, contrasting with our predictions, habitual rapid eye movement sleep was associated with an increase rather than decrease in anxiety. We discuss these results in terms of the rapid eye movement recalibration hypothesis, which suggests rapid eye movement sleep modulates activity in stress-encoding areas in the brain, leading to both decreased sensitivity and increased selectivity of stress responses.

Sleep consolidation potentiates sensorimotor adaptation.

Contrary to its well-established role in declarative learning, the impact of sleep on motor memory consolidation remains a subject of debate. Current literature suggests that while motor skill learning benefits from sleep, consolidation of sensorimotor adaptation (SMA) depends solely on the passage of time. This has led to the proposal that SMA may be an exception to other types of memories. Here, we addressed this ongoing controversy in humans through three comprehensive experiments using the visuomotor adaptation paradigm (N=290, 150 females). In Experiment 1, we investigated the impact of sleep on memory retention when the temporal gap between training and sleep was not controlled. In line with the previous literature, we found that memory consolidates with the passage of time. In Experiment 2, we used an anterograde interference protocol to determine the time window during which SMA memory is most fragile and, thus, potentially most sensitive to sleep intervention. Our results show that memory is most vulnerable during the initial hour post-training. Building on this insight, in Experiment 3 we investigated the impact of sleep when it coincided with the critical first hour of memory stabilization. This manipulation unveiled a benefit of sleep (30% memory enhancement) alongside an increase in spindle density and spindle-SO coupling during NREM sleep, two well-established neural markers of sleep consolidation. Our findings reconcile seemingly conflicting perspectives on the active role of sleep in motor learning and point to common mechanisms at the basis of memory formation.Significance statement While there is compelling evidence that sleep improves declarative memory, its role in the consolidation of motor memories remains a long-standing debate. For example, it is currently established that sensorimotor adaptation (SMA) consolidates with the passage of time, irrespective of sleep. This has led to the proposal that SMA may be an exception to other types of memories. Our findings indicate that SMA memories may indeed consolidate with both the passage of time and sleep, depending on the proximity between training and bedtime. Our work sheds light on this controversy and points to the existence of common mechanisms supporting consolidation across memory domains. Furthermore, it may impact rehabilitation programs, expediting motor injury recovery by aligning training sessions with sleep.

An automated sleep staging tool based on simple statistical features of mice electroencephalography (EEG) and electromyography (EMG) data.

Electroencephalogram (EEG) and electromyogram (EMG) are fundamental tools in sleep research. However, investigations into the statistical properties of rodent EEG/EMG signals in the sleep-wake cycle have been limited. The lack of standard criteria in defining sleep stages forces researchers to rely on human expertise to inspect EEG/EMG. The recent increasing demand for analysing large-scale and long-term data has been overwhelming the capabilities of human experts. In this study, we explored the statistical features of EEG signals in the sleep-wake cycle. We found that the normalized EEG power density profile changes its lower and higher frequency powers to a comparable degree in the opposite direction, pivoting around 20-30 Hz between the NREM sleep and the active brain state. We also found that REM sleep has a normalized EEG power density profile that overlaps with wakefulness and a characteristic reduction in the EMG signal. Based on these observations, we proposed three simple statistical features that could span a 3D space. Each sleep-wake stage formed a separate cluster close to a normal distribution in the 3D space. Notably, the suggested features are a natural extension of the conventional definition, making it useful for experts to intuitively interpret the EEG/EMG signal alterations caused by genetic mutations or experimental treatments. In addition, we developed an unsupervised automatic staging algorithm based on these features. The developed algorithm is a valuable tool for expediting the quantitative evaluation of EEG/EMG signals so that researchers can utilize the recent high-throughput genetic or pharmacological methods for sleep research.

Network signatures define consciousness state during focal seizures.

Epilepsy is a common neurological disorder affecting 1% of the global population. Loss of consciousness in focal impaired awareness seizures (FIASs) and focal-to-bilateral tonic-clonic seizures (FBTCSs) can be devastating, but the mechanisms are not well understood. Although ictal activity and interictal connectivity changes have been noted, the network states of focal aware seizures (FASs), FIASs, and FBTCSs have not been thoroughly evaluated with network measures ictally. We obtained electrographic data from 74 patients with stereoelectroencephalography (SEEG). Sliding window band power, functional connectivity, and segregation were computed on preictal, ictal, and postictal data. Five-minute epochs of wake, rapid eye movement sleep, and deep sleep were also extracted. Connectivity of subcortical arousal structures was analyzed in a cohort of patients with both SEEG and functional magnetic resonance imaging (fMRI). Given that custom neuromodulation of seizures is predicated on detection of seizure type, a convolutional neural network was used to classify seizure types. We found that in the frontoparietal association cortex, an area associated with consciousness, both consciousness-impairing seizures (FIASs and FBTCSs) and deep sleep had increases in slow wave delta (1-4 Hz) band power. However, when network measures were employed, we found that only FIASs and deep sleep exhibited an increase in delta segregation and a decrease in gamma segregation. Furthermore, we found that only patients with FIASs had reduced subcortical-to-neocortical functional connectivity with fMRI versus controls. Finally, our deep learning network demonstrated an area under the curve of .75 for detecting consciousness-impairing seizures. This study provides novel insights into ictal network measures in FASs, FIASs, and FBTCSs. Importantly, although both FIASs and FBTCSs result in loss of consciousness, our results suggest that ictal network changes in FIASs uniquely resemble those that occur during deep sleep. Our results may inform novel neuromodulation strategies for preservation of consciousness in epilepsy.

Perioperative Staff’s Experiences of Premedication for Children

PurposeProviding perioperative care for children who express anxiety or react with negative anxiety-associated consequences can be a challenge. The use of premedication is established as an important intervention for young children before surgery, yet research into care providers’ experiences of premedication is limited. The aim of this study was to explore perioperative staff’s experiences of premedication for preschool-age children. DesignA descriptive inductive qualitative study was performed based on focus group discussions. MethodsA purposive sample of a team from the operating department with experience in anesthetizing and caring for children in the perioperative period was interviewed in small focus groups: five preoperative and postoperative care nurses, five nurse anesthetists, and five anesthesiologists. The transcribed text was categorized using qualitative content analysis. FindingsThe content analysis revealed three themes: a matter of time, do not wake the sleeping bear, and on responsive tiptoes. ConclusionsCare providers must adapt their work to the child’s emotional state of mind and needs, allowing time for the child to trust and accept the premedication and for the premedication to reach its peak effect. Premedication provides light sleep preoperatively, which requires careful treatment of the child to avoid emotional reactions, and the postoperative path is most peaceful when the premedication supports a long duration of sedation. Our findings highlight the need for safety precautions and a permissive and flexible organization with the goal of achieving a smooth and safe journey for the child in the perioperative path.

Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet.

The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achieve optimal classification performance due to a paucity of raw data. To leverage the data characteristics and enhance the classification accuracy, we propose and evaluate a novel dual-link deep neural network model, 'DoubleLinkSleepCLNet'. The DoubleLinkSleepCLNet model performs feature extraction and efficient classification on both the raw EEG and the EEG processed with the Hilbert transform. It leverages the frequency domain and time domain feature modules, resulting in superior performance compared to other models. The DoubleLinkSleepCLNet model, using the 2 Raw/2 Hilbert data modes, achieved the highest classification performance with an accuracy of 88.47%. The average accuracy of the EEG was improved by approximately 4.08% after the application of the Hilbert transform. Additionally, Convolutional Neural Network (CNN) demonstrated superior performance in processing phase information, whereas Long Short-Term Memory (LSTM) excelled in handling time series data. The application of the Hilbert transform to EEG data, followed by processing it with a convolutional neural network, enhances the accuracy of the model. These findings introduce novel concepts for accelerating sleep stage prediction research, suggesting potential applications of these methods to other EEG analyses.

Characterisation of sleep apneas and respiratory circuitry in mice lacking CDKL5.

CDKL5 deficiency disorder is a rare genetic disease caused by mutations in the CDKL5 gene. Central apneas during wakefulness have been reported in patients with CDKL5 deficiency disorder. Studies on CDKL5-knockout mice, a CDKL5 deficiency disorder model, reported sleep apneas, but it is still unclear whether these events are central (central sleep apnea) or obstructive (obstructive sleep apnea) and may be related to alterations of brain circuits that modulate breathing rhythm. This study aimed to discriminate central sleep apnea and obstructive sleep apnea in CDKL5-knockout mice, and explore changes in the somatostatin neurons expressing high levels of neurokinin-1 receptors within the preBötzinger complex. Ten adult male wild-type and 12 CDKL5-knockout mice underwent electrode implantation for sleep stage discrimination and diaphragmatic activity recording, and were studied using whole-body plethysmography for 7 hr during the light (resting) period. Sleep apneas were categorised as central sleep apnea or obstructive sleep apnea based on the recorded signals. The number of somatostatin neurons in the preBötzinger complex and their neurokinin-1 receptors expression were assessed through immunohistochemistry in a sub-group of animals. CDKL5-knockout mice exhibited a higher apnea occurrence rate and a greater prevalence of obstructive sleep apnea during rapid eye movement sleep, compared with wild-type, whereas no significant difference was observed for central sleep apnea. Moreover, CDKL5-knockout mice showed a reduced number of somatostatin neurons in the preBötzinger complex, and these neurons expressed a lower level of neurokinin-1 receptors compared with wild-type controls. These findings underscore the pivotal role of CDKL5 in regulating normal breathing, suggesting its potential involvement in shaping preBötzinger complex neural circuitry and controlling respiratory muscles during sleep.