[Separation/Conversion Disorders in Functional Coma With Pseudocataplexy: Report of One Case and Literature Review].

Separation/conversion disorders in functional coma with pseudocataplexy are rare.On December 9,2021,a young female patient with separation/conversion disorders was treated in the Department of Neurology in the First Affiliated Hospital of Shandong First Medical University.The main symptoms were episodic consciousness disorders,sudden fainting,and urinary incontinence.Complete laboratory tests and cranial magnetic resonance imaging showed no obvious abnormalities.Standard multi-channel sleep monitoring and multiple sleep latency tests were performed.The patient was unable to wake up during nap and underwent stimulation tests.There was no response to orbital pressure,loud calls,or tapping,while the α rhythm in all electroencephalogram leads and the increased muscular tone in the mandibular electromyography indicated a period of wakefulness.The results of 24-hour sleep monitoring suggested that the patient had sufficient sleep at night and thus was easy to wake up in the morning.The results of daytime unrestricted sleep and wake-up test showed that the patient took one nap in the morning and one nap in the afternoon.When the lead indicated the transition from N3 to N2 sleep,a wake-up test was performed on the patient.At this time,the patient reacted to the surrounding environment and answered questions correctly.Because the level of orexin in the cerebrospinal fluid was over 110 pg/mL,episodic sleep disorder was excluded and the case was diagnosed as functional coma accompanied by pseudocataplexy.The patient did not present obvious symptom remission after taking oral medication,and thus medication withdrawl was recommended.Meanwhile,the patient was introduced to adjust the daily routine and mood.The follow-up was conducted six months later,and the patient reported that she did not experience similar symptoms after adjusting lifestyle.Up to now,no similar symptoms have appeared in multiple follow-up visits for three years.Functional coma with pseudocataplexy is prone to misdiagnosis and needs to be distinguished from true coma and episodic sleep disorders.

Sleep characteristics and self-reported sleep quality in the oldest-old: Results from a prospective longitudinal cohort study.

Little is known about the correlation between subjective perception and objective measures of sleep quality in particular in the oldest-old. The aim of this study was to perform longitudinal home sleep monitoring in this age group, and to correlate results with self-reported sleep quality. This is a prospective longitudinal home sleep-monitoring study in 12 oldest-old persons (age 83-100 years, mean 93 years, 10 females) without serious sleep disorders over 1 month using a contactless piezoelectric bed sensor (EMFIT QS). Participants provided daily information about perceived sleep. Duration in bed: 264-639 min (M = 476 min, SD = 94 min); sleep duration: 239-561 min (M = 418 min, SD = 91 min); sleep efficiency: 83.9%-90.7% (M = 87.4%, SD = 5.0%); rapid eye movement sleep: 21.1%-29.0% (M = 24.9%, SD = 5.5%); deep sleep: 13.3%-19.6% (M = 16.8%, SD = 4.5%). All but one participant showed a weak (r = 0.2-0.39) or very weak (r = 0-0.19) positive or negative correlation between self-rated sleep quality and the sleep score. In conclusion, longitudinal sleep monitoring in the home of elderly people by a contactless piezoelectric sensor system is feasible and well accepted. Subjective perception of sleep quality does not correlate well with objective measures in our study. Our findings may help to develop new approaches to sleep problems in the oldest-old including home monitoring. Further studies are needed to explore the full potential of this approach.

[Development of an Intelligent Multi-Parameter Sleep Diagnosis and Analysis System].

Sleep disordered breathing (SDB) is a common sleep disorder with an increasing prevalence. The current gold standard for diagnosing SDB is polysomnography (PSG), but existing PSG techniques have some limitations, such as long manual interpretation times, a lack of data quality control, and insufficient monitoring of gas metabolism and hemodynamics. Therefore, there is an urgent need in China's sleep clinical applications to develop a new intelligent PSG system with data quality control, gas metabolism assessment, and hemodynamic monitoring capabilities. The new system, in terms of hardware, detects traditional parameters like nasal airflow, blood oxygen levels, electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), electrooculogram (EOG), and includes additional modules for gas metabolism assessment via end-tidal CO 2 and O 2 concentration, and hemodynamic function assessment through impedance cardiography. On the software side, deep learning methods are being employed to develop intelligent data quality control and diagnostic techniques. The goal is to provide detailed sleep quality assessments that effectively assist doctors in evaluating the sleep quality of SDB patients.

What Radio Waves Tell Us about Sleep!

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=880) demonstrate that the model captures the sleep hypnogram (with an accuracy of 80.5% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.89), and measures the patient's Apnea-Hypopnea Index (ICC=0.90; 95% CI = [0.88, 0.91]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.

Advancements in Flexible Sensors for Monitoring Body Movements during Sleep: A Review.

Sleep plays a role in maintaining our physical well-being. However, sleep-related issues impact millions of people globally. Accurate monitoring of sleep is vital for identifying and addressing these problems. While traditional methods like polysomnography (PSG) are commonly used in settings, they may not fully capture natural sleep patterns at home. Moreover, PSG equipment can disrupt sleep quality. In recent years, there has been growing interest in the use of sensors for sleep monitoring. These lightweight sensors can be easily integrated into textiles or wearable devices using technology. The flexible sensors can be designed for skin contact to offer continuous monitoring without being obtrusive in a home environment. This review presents an overview of the advancements made in flexible sensors for tracking body movements during sleep, which focus on their principles, mechanisms, and strategies for improved flexibility, practical applications, and future trends.

A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals.

People spend approximately one-third of their lives in sleep, but more and more people are suffering from sleep disorders. Sleep posture is closely related to sleep quality, so related detection is very significant. In our previous work, a smart flexible sleep monitoring belt with MEMS triaxial accelerometer and pressure sensor has been developed to detect the vital signs, snore events and sleep stages. However, the method for sleep posture detection has not been studied. Therefore, to achieve high performance, low cost and comfortable experience, this paper proposes a smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals measured by a MEMS Inertial Measurement Unit (IMU). Statistical analysis and wavelet packet transform are applied for the feature extraction of the vital sign signals. Then the algorithm of recursive feature elimination with cross-validation is introduced to further extract the key features. Besides, machine learning models with 10-fold cross validation process, such as decision tree, random forest, support vector machine, extreme gradient boosting and adaptive boosting, were adopted to recognize the sleep posture. 15 subjects were recruited to participate the experiment. Experimental results demonstrate that the detection accuracy of the random forest algorithm is the highest among the five machine learning models, which reaches 96.02 %. Therefore, the proposed sleep posture detection method based on the flexible sleep monitoring belt is feasible and effective.

Evaluation of Lateral Radar Positioning for Vital Sign Monitoring: An Empirical Study.

Vital sign monitoring is dominated by precise but costly contact-based sensors. Contactless devices such as radars provide a promising alternative. In this article, the effects of lateral radar positions on breathing and heartbeat extraction are evaluated based on a sleep study. A lateral radar position is a radar placement from which multiple human body zones are mapped onto different radar range sections. These body zones can be used to extract breathing and heartbeat motions independently from one another via these different range sections. Radars were positioned above the bed as a conventional approach and on a bedside table as well as at the foot end of the bed as lateral positions. These positions were evaluated based on six nights of sleep collected from healthy volunteers with polysomnography (PSG) as a reference system. For breathing extraction, comparable results were observed for all three radar positions. For heartbeat extraction, a higher level of agreement between the radar foot end position and the PSG was found. An example of the distinction between thoracic and abdominal breathing using a lateral radar position is shown. Lateral radar positions could lead to a more detailed analysis of movements along the body, with the potential for diagnostic applications.

Sleep After Concussion: A Scoping Review of Sensor Technologies.

Sleep disturbances following a concussion/mild traumatic brain injury are associated with longer recovery times and more comorbidities. Sensor technologies can directly monitor sleep-related physiology and provide objective sleep metrics. This scoping review determines how sensor technologies are currently used to monitor sleep following a concussion. We searched Ovid (Medline, Embase), Web of Science, CINAHL, Compendex Engineering Village, and PsycINFO from inception to June 20, 2022, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for scoping reviews. Included studies objectively monitored sleep in participants with concussion. We screened 1081 articles and included 37 in the review. A total of 17 studies implemented polysomnography (PSG) months to years after injury for a median of two nights and provided a wide range of sleep metrics, including sleep-wake times, sleep stages, arousal indices, and periodic limb movements. Twenty-two studies used actigraphy days to weeks after injury for a median of 10 days and nights and provided information limited to sleep-wake times. Sleep stages were most reported in PSG studies, and sleep efficiency was most reported in actigraphy studies. For both technologies there was high variability in reported outcome measures. Sleep sensing technologies may be used to identify how sleep affects concussion recovery. However, high variability in sensor deployment methodologies makes cross-study comparisons difficult and highlights the need for standardization. Consensus on how sleep sensing technologies are used post-concussion may lead to clinical integration with subjective methods for improved sleep monitoring during the recovery period.

Validation of the Sleepiz One + as a radar-based sensor for contactless diagnosis of sleep apnea.

PURPOSE: The cardiorespiratory polysomnography (PSG) is an expensive and limited resource. The Sleepiz One + is a novel radar-based contactless monitoring device that can be used e.g. for longitudinal detection of nocturnal respiratory events. The present study aimed to compare the performance of the Sleepiz One + device to the PSG regarding the accuracy of apnea-hypopnea index (AHI). METHODS: From January to December 2021, a total of 141 adult volunteers who were either suspected of having sleep apnea or who were healthy sleepers took part in a sleep study. This examination served to validate the Sleepiz One + device in the presence and absence of additional SpO2 information. The AHI determined by the Sleepiz One + monitor was estimated automatically and compared with the AHI derived from manual PSG scoring. RESULTS: The correlation between the Sleepiz-AHI and the PSG-AHI with and without additional SpO2 measurement was rp = 0.94 and rp = 0,87, respectively. In general, the Bland-Altman plots showed good agreement between the two methods of AHI measurement, though their deviations became larger with increasing sleep-disordered breathing. Sensitivity and specificity for recordings without additional SpO2 was 85% and 88%, respectively. Adding a SpO2 sensor increased the sensitivity to 88% and the specificity to 98%. CONCLUSION: The Sleepiz One + device is a valid diagnostic tool for patients with moderate to severe OSA. It can also be easily used in the home environment and is therefore beneficial for e.g. immobile and infectious patients. TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION FOR PROSPECTIVELY REGISTERED TRIALS: This study was registered on clinicaltrials.gov (NCT04670848) on 2020-12-09.

Jerks are Useful: Extracting pulse rate from wrist-placed accelerometry jerk during sleep in children.

STUDY OBJECTIVES: Evaluate wrist-placed accelerometry predicted heartrate compared to electrocardiogram (ECG) heartrate in children during sleep. METHODS: Children (n=82, 61% male, 43.9% Black) wore a wrist-placed Apple Watch Series 7 (AWS7) and ActiGraph GT9X during a polysomnogram. 3-Axis accelerometry data was extracted from AWS7 and the GT9X. Accelerometry heartrate estimates were derived from jerk (the rate of acceleration change), computed using the peak magnitude frequency in short time Fourier Transforms of Hilbert transformed jerk computed from acceleration magnitude. Heartrates from ECG traces were estimated from R-R intervals using R-pulse detection. Lin's Concordance Correlation Coefficient (CCC), mean absolute error (MAE) and mean absolute percent error (MAPE) assessed agreement with ECG estimated heartrate. Secondary analyses explored agreement by polysomnography sleep stage and a signal quality metric. RESULTS: The developed scripts are available on Github. For the GT9X, CCC was poor at -0.11 and MAE and MAPE were high at 16.8 (SD=14.2) beats/minute and 20.4% (SD=18.5%). For AWS7, CCC was moderate at 0.61 while MAE and MAPE were lower at 6.4 (SD=9.9) beats/minute and 7.3% (SD=10.3%). Accelerometry estimated heartrate for AWS7 was more closely related to ECG heartrate during N2, N3 and REM sleep than lights on, wake, and N1 and when signal quality was high. These patterns were not evident for the GT9X. CONCLUSIONS: Raw accelerometry data extracted from AWS7, but not the GT9X, can be used to estimate heartrate in children while they sleep. Future work is needed to explore the sources (i.e., hardware, software, etc.) of the GT9X's poor performance.

Assessing sleep-related breathing disorders among newly diagnosed rheumatoid and psoriatic arthritis patients: a cross-sectional study.

OBJECTIVES: This cross-sectional study aimed to determine the prevalence and risk factors for sleep-related breathing disorders (SRBD) in newly diagnosed, untreated rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients, and to develop a screening algorithm for early detection. METHODS: We evaluated newly diagnosed RA or PsA patients using the Epworth Sleepiness Scale (ESS) questionnaire, cardiorespiratory polygraphy (RPG), and clinical and laboratory assessments. Sleep apnea syndrome (SAS) was diagnosed based on pathological RPG findings excessive daytime sleepiness, defined as ESS score above 10. RESULTS: The study included 39 patients (22 RA, 17 PsA) and 23 controls. In RPG, SRBD was identified in 38.5% of arthritis patients compared to 39.1% of controls (p = 1.00), with male gender (p = .004) and age (p < .001) identified as risk factors. Excessive daytime sleepiness was noted in 36.4% of RA patients, 17.6% of PsA patients, and 21.7% of controls. Of the 24 patients diagnosed with SRBD, 41.6% met the criteria for SAS. SAS prevalence was 31.8% among RA patients, 0% in PsA patients, and 13% in controls. A significant association was observed between excessive daytime sleepiness and SRBD (p = .036). CONCLUSION: Our findings reveal a high prevalence of SRBD in newly diagnosed, untreated RA and PsA patients in ESS and RPG, with excessive daytime sleepiness being a reliable predictor of SRBD. Patients with RA exhibited a higher predisposition to SAS. We therefore suggest incorporating ESS and RPG as screening tools in RA or PsA for early detection and management of SRBD.

Prevalence of obstructive sleep apnea in patients with chronic obstructive pulmonary disease in Vietnam.

PURPOSE: Chronic obstructive pulmonary disease and obstructive sleep apnea are two common respiratory diseases. Chronic obstructive pulmonary disease patients co-morbid with obstructive sleep apnea are associated with increased cardiovascular adverse events, frequent acute exacerbations, and higher mortality. Only a few studies on obstructive sleep apnea among patients with chronic obstructive pulmonary disease are available in Vietnam. The study aims to determine the prevalence of obstructive sleep apnea in patients with chronic obstructive pulmonary disease in Vietnam. METHODS: This is a cross-sectional study in patients with chronic obstructive pulmonary disease at multi-sites in Vietnam: the People's Hospital of Gia Dinh, Bach Mai Hospital, Phoi Viet Clinics, and Lam Dong Medical College using type 3 sleep monitoring device at sleep labs to diagnose obstructive sleep apnea in all study participants. RESULTS: Two hundred seventy-eight patients with chronic obstructive pulmonary disease were enrolled. Among the patients, 93.2% were male, with an average age of 66.9 ± 9.3 and a BMI of 21.9 ± 3.8 kg/m2; 82.0% were symptomatic including 44.6% in group B and 37.4% in group D with average post-FEV1 of 49.8 ± 18.3% predicted values. One hundred seventeen patients (42.1%) with chronic obstructive pulmonary disease presented obstructive sleep apnea defined by AHI ≥ 15 events/h. CONCLUSIONS: The prevalence of obstructive sleep apnea in patients with chronic obstructive pulmonary disease in Vietnam was 42.1% for an AHI of ≥ 15 events/h.

The Ultra-Long-Term Sleep study: Design, rationale, data stability and user perspective.

Sleep deprivation and poor sleep quality are significant societal challenges that negatively impact individuals' health. The interaction between subjective sleep quality, objective sleep measures, physical and cognitive performance, and their day-to-day variations remains poorly understood. Our year-long study of 20 healthy individuals, using subcutaneous electroencephalography, aimed to elucidate these interactions, assessing data stability and participant satisfaction, usability, well-being and adherence. In the study, 25 participants were fitted with a minimally invasive subcutaneous electroencephalography lead, with 20 completing the year of subcutaneous electroencephalography recording. Signal stability was measured using covariance of variation. Participant satisfaction, usability and well-being were measured with questionnaires: Perceived Ease of Use questionnaire, System Usability Scale, Headache questionnaire, Major Depression Inventory, World Health Organization 5-item Well-Being Index, and interviews. The subcutaneous electroencephalography signals remained stable for the entire year, with an average participant adherence rate of 91%. Participants rated their satisfaction with the subcutaneous electroencephalography device as easy to use with minimal or no discomfort. The System Usability Scale score was high at 86.3 ± 10.1, and interviews highlighted that participants understood how to use the subcutaneous electroencephalography device and described a period of acclimatization to sleeping with the device. This study provides compelling evidence for the feasibility of longitudinal sleep monitoring during everyday life utilizing subcutaneous electroencephalography in healthy subjects, showcasing excellent signal stability, adherence and user experience. The amassed subcutaneous electroencephalography data constitutes the largest dataset of its kind, and is poised to significantly advance our understanding of day-to-day variations in normal sleep and provide key insights into subjective and objective sleep quality.

Remote Wearable Neuroimaging Devices for Health Monitoring and Neurophenotyping: A Scoping Review.

Digital health tracking is a source of valuable insights for public health research and consumer health technology. The brain is the most complex organ, containing information about psychophysical and physiological biomarkers that correlate with health. Specifically, recent developments in electroencephalogram (EEG), functional near-infra-red spectroscopy (fNIRS), and photoplethysmography (PPG) technologies have allowed the development of devices that can remotely monitor changes in brain activity. The inclusion criteria for the papers in this review encompassed studies on self-applied, remote, non-invasive neuroimaging techniques (EEG, fNIRS, or PPG) within healthcare applications. A total of 23 papers were reviewed, comprising 17 on using EEGs for remote monitoring and 6 on neurofeedback interventions, while no papers were found related to fNIRS and PPG. This review reveals that previous studies have leveraged mobile EEG devices for remote monitoring across the mental health, neurological, and sleep domains, as well as for delivering neurofeedback interventions. With headsets and ear-EEG devices being the most common, studies found mobile devices feasible for implementation in study protocols while providing reliable signal quality. Moderate to substantial agreement overall between remote and clinical-grade EEGs was found using statistical tests. The results highlight the promise of portable brain-imaging devices with regard to continuously evaluating patients in natural settings, though further validation and usability enhancements are needed as this technology develops.

[Development of Wireless Wearable Sleep Monitoring System Based on EEG Signal].

A wireless wearable sleep monitoring system based on EEG signals is developed. The collected EEG signals are wirelessly sent to the PC or mobile phone Bluetooth APP for real-time display. The system is small in size, low in power consumption, and light in weight. It can be worn on the patient's forehead and is comfortable. It can be applied to home sleep monitoring scenarios and has good application value. The key performance indicators of the system are compared with the industry-related medical device measurement standards, and the measurement results are better than the special standards.

Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity.

STUDY OBJECTIVES: The gold standard for diagnosing obstructive sleep apnea (OSA) is polysomnography (PSG). However, PSG is a time-consuming method with clinical limitations. This study aimed to create a wireless radar framework to screen the likelihood of 2 levels of OSA severity (ie, moderate-to-severe and severe OSA) in accordance with clinical practice standards. METHODS: We conducted a prospective, simultaneous study using a wireless radar system and PSG in a Northern Taiwan sleep center, involving 196 patients. The wireless radar sleep monitor, incorporating hybrid models such as deep neural decision trees, estimated the respiratory disturbance index relative to the total sleep time established by PSG (RDIPSG_TST), by analyzing continuous-wave signals indicative of breathing patterns. Analyses were performed to examine the correlation and agreement between the RDIPSG_TST and apnea-hypopnea index, results obtained through PSG. Cut-off thresholds for RDIPSG_TST were determined using Youden's index, and multiclass classification was performed, after which the results were compared. RESULTS: A strong correlation (ρ = 0.91) and agreement (average difference of 0.59 events/h) between apnea-hypopnea index and RDIPSG_TST were identified. In terms of the agreement between the 2 devices, the average difference between PSG-based apnea-hypopnea index and radar-based RDIPSG_TST was 0.59 events/h, and 187 out of 196 cases (95.41%) fell within the 95% confidence interval of differences. A moderate-to-severe OSA model achieved an accuracy of 90.3% (cut-off threshold for RDIPSG_TST: 19.2 events/h). A severe OSA model achieved an accuracy of 92.4% (cut-off threshold for RDIPSG_TST: 28.86 events/h). The mean accuracy of multiclass classification performance using these cut-off thresholds was 83.7%. CONCLUSIONS: The wireless-radar-based sleep monitoring device, with cut-off thresholds, can provide rapid OSA screening with acceptable accuracy and also alleviate the burden on PSG capacity. However, to independently apply this framework, the function of determining the radar-based total sleep time requires further optimizations and verification in future work. CITATION: Lin S-Y, Tsai C-Y, Majumdar A, et al. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024;20(8):1267-1277.

Assessing the immediate impact of Twin-block appliance insertion on adolescents' sleep using a wearable device.

OBJECTIVE: This prospective clinical study aimed to evaluate the immediate impact of Twin-block appliance insertion on the sleep of adolescents using a wearable device. MATERIALS AND METHODS: A total of 24 girls, aged 11-13 years, with Class II division 1 molar relationship, skeletal class 2 malocclusion (ANB ≥5) and overjet measuring ≥5 mm were selected. Exclusion criteria included a history of previous orthodontic treatment, systemic disease, irregular sleep pattern, obstructive sleep apnea, medical history of breathing disorders, or concurrent use of medications. Participants wore a wearable device to measure sleep parameters, including deep sleep, light sleep, minutes awake during sleep, wake-up times, bedtimes and total sleep times. The participants wore the device for 10 days prior to Twin-block insertion and sleep data were collected for another 10 days after insertion. RESULTS: Following the insertion of the Twin-block appliance, there was a highly statistically significant shift in bedtime and wake-up time to later hours (P < .001). All participants experienced a highly significant delay in bedtime compared to the recommended 10 pm time (P < .001). Additionally, there was a significant increase in the duration of light sleep (P < .05). However, the effect on deep sleep, minutes awake during sleep and sleep duration was not statistically significant. None of the sleep parameters tested showed statistically significant changes between the first 5 days after Twin-block insertion with the subsequent 5 days. CONCLUSION: The immediate insertion of the Twin-block appliance disrupts sleep onset, wake-up time and light sleep during the specified period of 10 days.

Sleep physiology in patients with epilepsy: Influence of seizures on rapid eye movement (REM) latency and REM duration.

OBJECTIVE: A well-established bidirectional relationship exists between sleep and epilepsy. Patients with epilepsy tend to have less efficient sleep and shorter rapid eye movement (REM) sleep. Seizures are far more likely to arise from sleep transitions and non-REM sleep compared to REM sleep. Delay in REM onset or reduction in REM duration may have reciprocal interactions with seizure occurrence. Greater insight into the relationship between REM sleep and seizure occurrence is essential to our understanding of circadian patterns and predictability of seizure activity. We assessed a cohort of adults undergoing evaluation of drug-resistant epilepsy to examine whether REM sleep prior to or following seizures is delayed in latency or reduced in quantity. METHODS: We used a spectrogram-guided approach to review the video-electroencephalograms of patients' epilepsy monitoring unit admissions for sleep scoring to determine sleep variables. RESULTS: In our cohort of patients, we found group- and individual-level delay of REM latency and reduced REM duration when patients experienced a seizure before the primary sleep period (PSP) of interest or during the PSP of interest. A significant increase in REM latency and decrease in REM quantity were observed on nights where a seizure occurred within 4 h of sleep onset. No change in REM variables was found when investigating seizures that occurred the day after the PSP of interest. Our study is the first to provide insight about a perisleep period, which we defined as 4-h periods before and after the PSP. SIGNIFICANCE: Our results demonstrate a significant relationship between seizures occurring prior to the PSP, during the PSP, and in the 4-h perisleep period and a delay in REM latency. These findings have implications for developing a biomarker of seizure detection as well as longer term seizure risk monitoring.

Case report: A new treatment for restless leg syndrome: three cases.

Restless legs syndrome is a movement disorder that seriously affects the quality of life of patients. It is characterized by marked discomfort mainly occurring in the deep tissues of the lower extremities, including deep muscle or bone chafing, as well as crawling sensations or pulling sensations. These sensations often cause patients to awaken after falling asleep and to feel the urge to walk around, which seriously affects their sleep quality. Patients with restless leg syndrome exhibit significantly enhanced sympathetic nerve activity and immune disorders, while stellate ganglion blockage can block sympathetic nerves and regulate immune cells and cytokines to maintain immune system homeostasis. We report three patients with restless legs syndrome complicated with severe nephrotic syndrome. After treatment with stellate ganglion block, the symptoms in the restless legs were relieved within 1 month, and the quality of sleep was significantly improved. Our findings suggest that stellate ganglion block has broad promise in the management of restless legs syndrome patients with severe comorbidities.

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment.

Autonomous sleep tracking at home has become inevitable in today's fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO-XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model's performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time-frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.