Traditional Sleep Research Articles

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring.

Sleep stage scoring in rodents is the process of identifying the three stages: nonrapid eye movement sleep (NREM), rapid eye movement sleep (REM), and wake. Sleep stage scoring is crucial for studying sleep stage-specific measures and effects. Sleep patterns in rodents differ from those in humans, characterized by shorter episodes of NREM and REM interspaced by waking, and traditional manual sleep stage scoring by human experts is time-consuming. To address this issue, previous studies have used machine learning-based approaches to develop algorithms to automatically categorize sleep stages, but high-performing models with great generalizability are often not publicly available/cost-free nor user-friendly for non-trained sleep researchers. Therefore, we developed a machine learning-based LightGBM algorithm trained with a large dataset. To make the model available to sleep researchers without coding experience, a software tool named IntelliSleepScorer (v1.2- newest version) was developed based on the model, which features an easy-to-use graphic user interface. In this manuscript, we present step-by-step instructions for using the software to demonstrate a convenient and effective automatic sleep stage scoring tool in mice for sleep researchers.

Exploring bispectral index as an alternative to polysomnography for long-term sleep monitoring in disorders of consciousness.

Sleep plays a crucial role in neuroplasticity and recovery from brain injury, particularly in disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Traditional sleep monitoring methods like polysomnography (PSG) are complex and often impractical for long-term use in clinical settings. This study aimed to explore the utility of the Bispectral Index (BIS) as a more practical alternative for monitoring sleep patterns in DoC patients. We conducted simultaneous PSG and BIS monitoring on 38 DoC patients (19 UWS and 19 MCS). The study focused on analyzing sleep timing distribution, the effectiveness of BIS in differentiating sleep stages, and its correlation with consciousness levels. Our findings revealed that DoC patients exhibited irregular and fragmented sleep patterns, necessitating extended monitoring periods. The BIS effectively differentiated various sleep stages, with significant differences in BIS values observed across these stages. However, BIS values did not show significant differences between UWS and MCS patients, indicating that BIS primarily indicates wakefulness rather than cognitive awareness. DoC patients have disturbed sleep-wake cycles that require prolonged monitoring. BIS can well distinguish sleep stages in DoC patients, and the distribution of values is similar to that of normal subjects. However, BIS could not distinguish the level of consciousness of DoC patients. The study demonstrates the potential of BIS as a practical tool for long-term sleep monitoring in DoC patients, offering a less intrusive alternative to traditional methods.

Automatic sleep stage classification using deep learning: signals, data representation, and neural networks

In clinical practice, sleep stage classification (SSC) is a crucial step for physicians in sleep assessment and sleep disorder diagnosis. However, traditional sleep stage classification relies on manual work by sleep experts, which is time-consuming and labor-intensive. Faced with this obstacle, computer-aided diagnosis (CAD) has the potential to become an intelligent assistant tool for sleep experts, aiding doctors in the assessment and decision-making process. In fact, in recent years, CAD supported by artificial intelligence, especially deep learning (DL) techniques, has been widely applied in SSC. DL offers higher accuracy and lower costs, making a significant impact. In this paper, we will systematically review SSC research based on DL methods (DL-SSC). We explores DL-SSC from several important perspectives, including signal and data representation, data preprocessing, deep learning models, and performance evaluation. Specifically, this paper addresses three main questions: (1) What signals can DL-SSC use? (2) What are the various methods to represent these signals? (3) What are the effective DL models? Through addressing on these questions, this paper will provide a comprehensive overview of DL-SSC.

Comparing sleep measures in cancer survivors: self-reported sleep diary versus objective wearable sleep tracker.

Cancer survivors are increasingly using wearable fitness trackers, but it is unclear if they match traditional self-reported sleep diaries. We aimed to compare sleep data from Fitbit and the Consensus Sleep Diary (CSD) in this group. We analyzed data from two randomized clinical trials, using both CSD and Fitbit to collect sleep outcomes: total sleep time (TST), wake time after sleep onset (WASO), number of awakenings (NWAK), time in bed (TIB), and sleep efficiency (SE). Insomnia severity was measured by Insomnia Severity Index (ISI). We used the Wilcoxon signed rank test, Spearman's rank correlation coefficients, and the Mann-Whitney test to compare sleep outcomes and assess their ability to distinguish insomnia severity levels between CSD and Fitbit data. Among 62 participants, compared to CSD, Fitbit recorded longer TST by an average of 14.6 (SD = 84.9) minutes, longer WASO by an average of 28.7 (SD = 40.5) minutes, more NWAK by an average of 16.7 (SD = 6.6) times per night, and higher SE by an average of 7.1% (SD = 14.4); but shorter TIB by an average of 24.4 (SD = 71.5) minutes. All the differences were statistically significant (all p < 0.05), except for TST (p = 0.38). Moderate correlations were found for TST (r = 0.41, p = 0.001) and TIB (r = 0.44, p < 0.001). Compared to no/mild insomnia group, participants with clinical insomnia reported more NWAK (p = 0.009) and lower SE (p = 0.029) as measured by CSD, but there were no differences measured byFitbit. TST was the only similar outcome between Fitbit and CSD. Our study highlights the advantages, disadvantages, and clinical utilization of sleep trackers in oncology.

Dietary supplementation with Lactium and L-theanine alleviates sleep disturbance in adults: a double-blind, randomized, placebo-controlled clinical study.

The use of natural products for the treatment of sleep disturbances is increasing owing to the side effects and limitations of traditional sleep therapy. Moreover, recent studies have shown a significant correlation between sleep quality and gut microbiota composition. This study aimed to assess the impact of LTC-022, a commercially available dietary supplement containing Lactium and L-theanine, on enhancing sleep quality. Forty participants experiencing sleep discomfort were enrolled in a double-blind randomized controlled trial, wherein they received LTC-022 or a placebo orally for 8 weeks. The effects of treatment on sleep quality were assessed using the Pittsburgh Sleep Quality Index and Insomnia Severity Index. To comprehensively evaluate changes in sleep patterns, various parameters were evaluated, including the time in bed (TIB), total sleep time (TST), sleep onset latency (SOL), sleep efficiency (SE), wake after sleep onset (WASO) counts, and bedtime. These parameters were derived from daily sleep logs recorded over the 8-week study period, categorized into weekdays and weekends. Stool samples were analyzed for microbiome composition. The V4 region of bacterial 16S rRNA genes was amplified using specific primers (515F and 806R) and targeted for analysis. Microbial diversity, including operational taxonomic units, the Shannon and Chao indices, the Firmicutes/Bacteroidetes (F/B) ratio, and the variety of bacterial taxa, was assessed. No significant differences were observed in sleep quality and insomnia scale characteristics between the two groups. In-depth analysis using sleep diaries showed that WASO counts after 8 weeks and bedtime after 4 weeks showed significant differences between the LTC-022 and control groups. In the LTC-022 group, significant differences were observed in the increase in TST, decrease in SOL, increase in SE, decrease in WASO counts, and earlier bedtime. Microbiome analysis revealed that the abundance of the genera Blautia and Ruminococcus increased in fecal samples from the LTC-022 group. These results suggest that continuous LTC-022 intake has a beneficial effect on maintaining sleep duration and an appropriate bedtime. Additionally, changes in the gut microbiota may be linked to changes in sleep patterns resulting from the consumption of Lactium and L-theanine. https://cris.nih.go.kr/cris/search/detailSearch.do/22841, KCT0007750.

A Novel Continuous Sleep State Artificial Neural Network Model Based on Multi-Feature Fusion of Polysomnographic Data.

Sleep structure is crucial in sleep research, characterized by its dynamic nature and temporal progression. Traditional 30-second epochs falter in capturing the intricate subtleties of various micro-sleep states. This paper introduces an innovative artificial neural network model to generate continuous sleep depth value (SDV), utilizing a novel multi-feature fusion approach with EEG data, seamlessly integrating temporal consistency. The study involved 50 normal and 100 obstructive sleep apnea-hypopnea syndrome (OSAHS) participants. After segmenting the sleep data into 3-second intervals, a diverse array of 38 feature values were meticulously extracted, including power, spectrum entropy, frequency band duration and so on. The ensemble random forest model calculated the timing fitness value for all the features, from which the top 7 time-correlated features were selected to create detailed sleep sample values ranging from 0 to 1. Subsequently, an artificial neural network (ANN) model was trained to delineate sleep continuity details, unravel concealed patterns, and far surpassed the traditional 5-stage categorization (W, N1, N2, N3, and REM). The SDV changes from wakeful stage (mean 0.7021, standard deviation 0.2702) to stage N3 (mean 0.0396, standard deviation 0.0969). During the arousal epochs, the SDV increases from the range (0.1 to 0.3) to the range around 0.7, and decreases below 0.3. When in the deep sleep (≤0.1), the probability of arousal of normal individuals is less than 10%, while the average arousal probability of OSA patients is close to 30%. A sleep continuity model is proposed based on multi-feature fusion, which generates SDV ranging from 0 to 1 (representing deep sleep to wakefulness). It can capture the nuances of the traditional five stages and subtle differences in microstates of sleep, considered as a complement or even an alternative to traditional sleep analysis.

Remote Evaluation of Sleep and Circadian Rhythms in Older Adults With Mild Cognitive Impairment and Dementia: Protocol for a Feasibility and Acceptability Mixed Methods Study.

Sleep disturbances are a potentially modifiable risk factor for neurodegenerative dementia secondary to Alzheimer disease (AD) and Lewy body disease (LBD). Therefore, we need to identify the best methods to study sleep in this population. This study will assess the feasibility and acceptability of various wearable devices, smart devices, and remote study tasks in sleep and cognition research for people with AD and LBD. We will deliver a feasibility and acceptability study alongside a prospective observational cohort study assessing sleep and cognition longitudinally in the home environment. Adults aged older than 50 years who were diagnosed with mild to moderate dementia or mild cognitive impairment (MCI) due to probable AD or LBD and age-matched controls will be eligible. Exclusion criteria include lack of capacity to consent to research, other causes of MCI or dementia, and clinically significant sleep disorders. Participants will complete a cognitive assessment and questionnaires with a researcher and receive training and instructions for at-home study tasks across 8 weeks. At-home study tasks include remote sleep assessments using wearable devices (electroencephalography headband and actigraphy watch), app-based sleep diaries, online cognitive assessments, and saliva samples for melatonin- and cortisol-derived circadian markers. Feasibility outcomes will be assessed relating to recruitment and retention, data completeness, data quality, and support required. Feedback on acceptability and usability will be collected throughout the study period and end-of-study interviews will be analyzed using thematic analysis. Recruitment started in February 2022. Data collection is ongoing, with final data expected in February 2024 and data analysis and publication of findings scheduled for the summer of 2024. This study will allow us to assess if remote testing using smart devices and wearable technology is a viable alternative to traditional sleep measurements, such as polysomnography and questionnaires, in older adults with and without MCI or dementia due to AD or LBD. Understanding participant experience and the barriers and facilitators to technology use for research purposes and remote research in this population will assist with the development of, recruitment to, and retention within future research projects studying sleep and cognition outside of the clinic or laboratory. DERR1-10.2196/52652.

Improving the detection of sleep slow oscillations in electroencephalographic data.

We aimed to build a tool which facilitates manual labeling of sleep slow oscillations (SOs) and evaluate the performance of traditional sleep SO detection algorithms on such a manually labeled data set. We sought to develop improved methods for SO detection. SOs in polysomnographic recordings acquired during nap time from ten older adults were manually labeled using a custom built graphical user interface tool. Three automatic SO detection algorithms previously used in the literature were evaluated on this data set. Additional machine learning and deep learning algorithms were trained on the manually labeled data set. Our custom built tool significantly decreased the time needed for manual labeling, allowing us to manually inspect 96,277 potential SO events. The three automatic SO detection algorithms showed relatively low accuracy (max. 61.08%), but results were qualitatively similar, with SO density and amplitude increasing with sleep depth. The machine learning and deep learning algorithms showed higher accuracy (best: 99.20%) while maintaining a low prediction time. Accurate detection of SO events is important for investigating their role in memory consolidation. In this context, our tool and proposed methods can provide significant help in identifying these events.

Pharmacological Treatments of Sleep–Wake Disorders: Update 2023

Biological, environmental, behavioral, and social factors can influence sleep and lead to sleep disorders or diseases. Sleep disorders are common, numerous, and heterogeneous in terms of their etiology, pathogenesis, and symptomatology. The management of sleep–wake circadian disorders (SWCDs) includes education on sleep hygiene, behavioral strategies, psychotherapy (cognitive behavioral therapy (CBT), particularly), instrument-based treatments (i.e., positive airway pressure therapy, hypoglossal nerve stimulation), and pharmacotherapy. Depending on the disease, therapy varies and is executed sequentially or can be a combination of several forms of therapy. Drugs used for SWCDs include traditional sleep- or wake-promoting agents and chronotherapeutic agents. Recently, novel medications, which more precisely act on specific neurochemical systems (i.e., the orexin system) important for sleep and waking, are also increasingly being used. In this review, the pharmacotherapy of common sleep disorders (insomnia, sleep-related breathing disorder, central disorders of hypersomnolence, circadian rhythm sleep–wake disorders, parasomnias, and sleep-related movement disorders) embedded in the overall therapeutic concept of each disorder is presented. There is also an outlook on possible future pharmacotherapies.

O049 A Quantitative EEG Analysis of Overnight Noise Exposure Effects on Sleep– A Laboratory Study

Abstract The impact of overnight wind farm noise exposure on sleep is unclear and traditional manual sleep scoring may not be sensitive enough to detect subtle changes in sleep. This study used quantitative EEG (qEEG) to assess subtle effects of noise on sleep under well-controlled laboratory conditions. Sixty-eight individuals (Males: Females; 30:38) aged (Median [IQR]) 55.5yrs [31.3-66.3], from four groups (rural residents with and without WFN-related complaints, control rural residents, and urban traffic noise-exposed residents) underwent overnight polysomnography during six different noise exposure nights in random order. Noises included 20sec and 3min intermittent road traffic and wind farm noise exposures (full night averages 42dBA and 32dBA, respectively), continuous and intermittent wind farm noise at 25dBA, and a quiet control night (background noise,19dBA). Traditional polysomnography sleep metrics, qEEG power in delta (0.5-4Hz) to beta (32Hz) frequencies, and K-complex density (events/min) were compared between noise conditions, groups and sleep stages using likelihood ratio tests. Wake after sleep onset, time spent in N1 sleep and relative beta activity were higher, whilst time spent in N3 was reduced on the 20 second noise exposure night compared to control (all p&amp;lt;0.05). Full night average qEEG outcomes were not different between nights. K-complex density was significantly increased during both 20sec and 3min intermittent noise exposure conditions compared to control (p&amp;lt;0.001). Full night qEEG outcomes do not appear to be more sensitive to noise related sleep disturbance than traditional sleep scoring methods. However, K-complex density assessments may be particularly useful in future studies of noise effects on sleep.

P003 Home-based sleep study paediatric model of care

Abstract Introduction An innovative service that was developed within a tertiary paediatric centre, involved the use of a home-based sleep study model of care (MOC) between March 2022 to September 2022. This MOC allowed low risk children to be able to undergo a sleep study earlier than, when compared to the traditional in-house sleep studies conducted within the hospital setting. Methods The MOC involved a sleep technician travelling to the patients’ home and then attaching sensors onto the child’s body. The at home sleep system used is a full, portable polysomnography system that gently simplifies the task of performing a sleep study while delivery secure and precise measurements from the comfort of the patient’s home. The system provides data collection via the use of electrocardiogram (ECG), electroencephalography (EEG), oximeter, plethsmography belts and facial electroculargraphs which are attached comfortably onto the patient’s body. An outpatient’s service run by Clinical Nurse Consultants then provided 24 hour support to families for troubleshooting overnight. Results A total of 49 home based sleep studies were performed during this period. Almost 90% of children tested had an abnormal sleep study result and 1 in 3 had moderate to severe findings. Eight studies (19%) were below the expected quality. This is comparable to in-hospital poor quality sleep study rates. Discussion The MOC reduced hospital admission for a diagnostic sleep study, provided quicker assessment and in the comfort of the child’s home.

Fascia Ecosystem: A Step Forward in Sleep Engineering and Research.

Millions suffer from sleep disorders, and sleep clinics and research institutions seek improved sleep study methods. This paper proposes the Fascia Ecosystem for Sleep Engineering to improve traditional sleep studies. The Fascia Sleep Mask is more comfortable and accessible than overnight stays at a sleep center, and the Fascia Portal and Fascia Hub allow for home-based sleep studies with real-time intervention and data analysis capabilities.A study of 10 sleep experts found that the Fascia Portal is easy to access, navigate, and use, with 44.4% finding it very easy to access, 33.3% very easy to navigate, and 60% very easy to get used to. Most experts found the Fascia Portal reliable and easy to use.Moreover, the study analyzed physiological signals during various states of sleep and wakefulness in two subjects. The results demonstrated that the Fascia dataset captured higher amplitude spindles in N2 sleep (72.20 V and 109.87 V in frontal and parietal regions, respectively) and higher peak-to-peak amplitude slow waves in N3 sleep (93.51 V) compared to benchmark datasets. Fascia produced stronger and more consistent EOG signals during REM sleep, indicating its potential to improve sleep disorder diagnosis and treatment by providing a deeper understanding of sleep patterns.

Coordinated human sleeping brainwaves map peripheral body glucose homeostasis

Insufficient sleep impairs glucose regulation, increasing the risk of diabetes. However, what it is about the human sleeping brain that regulates blood sugar remains unknown. In an examination of over 600 humans, we demonstrate that the coupling of non-rapid eye movement (NREM) sleep spindles and slow oscillations the night before is associated with improved next-day peripheral glucose control. We further show that this sleep-associated glucose pathway may influence glycemic status through altered insulin sensitivity, rather than through altered pancreatic beta cell function. Moreover, we replicate these associations in an independent dataset of over 1,900 adults. Of therapeutic significance, the coupling between slow oscillations and spindles was the most significant sleep predictor of next-day fasting glucose, even more so than traditional sleep markers, relevant to the possibility of an electroencephalogram (EEG) index of hyperglycemia. Taken together, these findings describe a sleeping-brain-body framework of optimal human glucose homeostasis, offering a potential prognostic sleep signature of glycemic control.

Acceptability & Usability of a Wearable Device for Sleep Health Among English- and Spanish-Speaking Patients in the Safety-Net: Qualitative Analysis.

Sleep disorders are common and disproportionately affect marginalized populations. Technology such as wearable devices holds the potential to improve sleep quality and reduce sleep disparities, but most devices have not been designed or tested with racially, ethnically, and socioeconomically diverse patients. Inclusion and engagement of diverse patients throughout digital health development and implementation are critical to achieving health equity. This study aims to evaluate the usability and acceptability of a wearable sleep monitoring device - SomnoRing® - and its accompanying mobile application among patients treated in a safety net clinic. The study team recruited English- and Spanish-speaking patients from a mid-sized pulmonary and sleep medicine practice serving publicly insured patients. Eligibility criteria included initial evaluation of obstructed sleep apnea which is most appropriate for limited cardiopulmonary testing. Patients with primary insomnia or other suspected sleep disorders were not included. Patients tested the SomnoRing® over a seven-night period and participated in a one-hour semi-structured virtual qualitative interview covering perceptions of the device, motivators and barriers to use, and general experiences with digital health tools. The study team used inductive/deductive processes to code interview transcripts, guided by the Technology Acceptance Model. Twenty-one individuals participated in the study. All participants owned a smartphone, almost all (19/21) felt comfortable using their phone, and few already owned a wearable (6/21). Almost all participants wore the SomnoRing® for seven nights and found it comfortable. Four themes emerged from qualitative data: 1) the SomnoRing® was easy to use compared to other wearable devices or traditional home sleep testing alternatives such as the standard polysomnogram technology for sleep studies; 2) the patient's context and environment such as family and peer influence, housing status, access to insurance, and device cost affected overall acceptance of the SomnoRing®; 3) clinical champions motivated use in supporting effective onboarding, interpretation of data, and, ongoing technical support; and 4) participants desired more assistance and information to best interpret their own sleep data summarized in the companion app. Racially, ethnically, and socioeconomically diverse patients with sleep disorders perceived a wearable as useful and acceptable for sleep health. Participants also uncovered external barriers related to the perceived usefulness of the technology, such as housing status, insurance coverage, and clinical support. Future studies should further examine how to best address these barriers so that wearables, such as the SomnoRing®, can be successfully implemented in the safety-net health setting. This manuscript does not report on a clinical trial.

0263 Feasibility of Measuring Sleep Spindles Using a Wearable EEG Headband in Adolescents with Neurodevelopmental Disorders

Abstract Introduction There is compelling evidence that sleep spindles, electroencephalogram (EEG) signatures of non-rapid eye movement sleep, are reliable markers of thalamocortical dysfunction in Schizophrenia. While promising, this research is limited by traditional sleep EEG methods, which cultivate small, non-representative samples. Wearable EEG headbands could be used to study naturalistic sleep across several nights in individuals who cannot tolerate standard sleep assessments, but this has yet to be tested. We aim to test feasibility of using a dry-EEG headband to measure sleep spindles in adolescents with a neurodevelopmental disorder, 22q11.2 Deletion Syndrome (22q11DS). Methods Adolescents with 22q11DS (n=7; 3 Females; Age: 15-28 years) were asked to sleep in their own bed wearing a dry-EEG headband (Dreem 3). Feasibility of data acquisition was determined by the ability of the participants to record at least 3 consecutive nights. We performed visual artifact detection and examined the ability to detect sleep spindles using the well-validated Ferrarelli Spindle Detection Algorithm on the F7-O2 electrode. Results All subjects completed at least 3 nights of recording (range: 3-5 nights), suggesting the device was well-tolerated. The data contained minimal artifact; 17 out of the 22 nights available for analysis contained artifact in &amp;lt; 20% of sleep epochs. The best night contained artifact in only 6% of sleep epochs. Sleep spindles were successfully detected using an automatic detector (M= 1.92 spindles/minute). Conclusion This pilot study suggests dry-EEG headbands offer a feasible method to measure sleep spindles in individuals with neurodevelopmental disorders. This new technology could allow for scalable biomarkers of atypical neurodevelopment associated with psychiatric disorders. Support (if any) RO1 MH085953; U01MH10171; UCLA Center for Autism Research Pilot Grant; Dreem Coup de Coeur Award

Energy consumption optimization of 5G base stations considering variable threshold sleep mechanism

The explosive growth of mobile data traffic has resulted in a significant increase in the energy consumption of 5G base stations (BSs). However, the existing energy conservation technologies, such as traditional BS sleep strategy, rarely consider the dynamic real-time changes of users (UEs), which may make it difficult to maximize sleep idle or lightly loaded BSs, thereby affecting the reduction of BS energy consumption. Therefore, an energy consumption optimization strategy of 5G BSs considering variable threshold sleep mechanism (ECOS-BS) is proposed in this paper. Compared to current strategies, the ECOS-BS strategy can dynamically adjust BSs’ optimal sleep threshold on the premise of considering UEs’ dynamic changes, which is conducive to maximize sleep available BSs and realize the optimal management of BS energy consumption. Taking a heterogeneous cellular network as an example, numerical simulations are conducted to validate the effectiveness of the proposed strategy.

A novel automated robust dual-channel EEG-based sleep scoring system using optimal half-band pair linear-phase biorthogonal wavelet filter bank.

Nowadays, the hectic work life of people has led to sleep deprivation. This may further result in sleep-related disorders and adverse physiological conditions. Therefore, sleep study has become an active research area. Sleep scoring is crucial for detecting sleep-related disorders like sleep apnea, insomnia, narcolepsy, periodic leg movement (PLM), and restless leg syndrome (RLS). Sleep is conventionally monitored in a sleep laboratory using polysomnography (PSG) which is the recording of various physiological signals. The traditional sleep stage scoring (SSG) done by professional sleep scorers is a tedious, strenuous, and time-consuming process as it is manual. Hence, developing a machine-learning model for automatic SSG is essential. In this study, we propose an automated SSG approach based on the biorthogonal wavelet filter bank's (BWFB) novel least squares (LS) design. We have utilized a huge Wisconsin sleep cohort (WSC) database in this study. The proposed study is a pioneering work on automatic sleep stage classification using the WSC database, which includes good sleepers and patients suffering from various sleep-related disorders, including apnea, insomnia, hypertension, diabetes, and asthma. To investigate the generalization of the proposed system, we evaluated the proposed model with the following publicly available databases: cyclic alternating pattern (CAP), sleep EDF, ISRUC, MIT-BIH, and the sleep apnea database from St. Vincent's University. This study uses only two unipolar EEG channels, namely O1-M2 and C3-M2, for the scoring. The Hjorth parameters (HP) are extracted from the wavelet subbands (SBS) that are obtained from the optimal BWFB. To classify sleep stages, the HP features are fed to several supervised machine learning classifiers. 12 different datasets have been created to develop a robust model. A total of 12 classification tasks (CT) have been conducted employing various classification algorithms. Our developed model achieved the best accuracy of 83.2% and Cohen's Kappa of 0.7345 to reliably distinguish five sleep stages, using an ensemble bagged tree classifier with 10-fold cross-validation using WSC data. We also observed that our system is either better or competitive with existing state-of-art systems when we tested with the above-mentioned five databases other than WSC. This method yielded promising results using only two EEG channels using a huge WSC database. Our approach is simple and hence, the developed model can be installed in home-based clinical systems and wearable devices for sleep scoring.

Timing of Blood Draws Among Patients Hospitalized in a Large Academic Medical Center

This study describes the degree to which blood draws occurred among hospitalized patients during traditional sleep hours and investigates trends over time.

A sleep scheduling algorithm of redundant nodes in power communication sensor networks based on relative local density

In order to overcome the problems of low network coverage and short network node survival time in the traditional node sleep scheduling algorithm, a redundant node sleep scheduling algorithm based on relative local density is proposed in this paper. The composition of power communication sensor network is analysed, and the energy receiving model of redundant nodes in power communication sensor network is constructed. The sleep scheduling of network redundant nodes is realised according to the relative local density redundant node sleep algorithm. The results show that when the number of nodes is 120, the network performance of this method is 5.18 and the network coverage can reach 99.93%. At the same time, the survival time of sensor nodes under this method is longer, this method can prolong the network life cycle, and the sleep scheduling effect of redundant nodes is better.

O020 Sleep regularity is a stronger predictor of all-cause mortality than sleep duration or sleep efficiency in ~60 000 UK Biobank participants

Abstract Sleep duration and sleep efficiency are observed risk factors for all-cause mortality, but the role of sleep regularity is unknown. Irregular sleep patterns are associated with ageing, cancer, depressed mood, and cardio-metabolic diseases, and may therefore increase risk of mortality. We extracted Sleep Regularity Index (SRI) scores, sleep duration, and sleep efficiency from one week of actigraphy in 60,997 UK Biobank participants (62.8±7.8 years, 55.6% female). Using Cox Proportional Hazards models, we assessed whether SRI, sleep duration, and sleep efficiency predicted all-cause mortality up to 7.6 years post-actigraphy. A one standard deviation increase in SRI score (+10.7), indicating more regular sleep patterns, predicted a 20.4±2.0% reduced hazard of all-cause mortality (p &amp;lt; .0001). This relationship remained robust after controlling for age, sex, ethnicity, education, income, and employment status (+1SD in SRI predicted an 18.7±2.4% hazard reduction, p &amp;lt; .0001). SRI was a relatively stronger predictor of mortality hazard than sleep duration (explaining 19.0±2.1% vs. 12.4±2.6% of hazard reduction) or sleep efficiency (19.0±2.1% vs. 13.1±2.6%) in two additional Cox models. Sleep regularity may play a causal role in mortality, via contribution to poor health outcomes, or it may be a marker of poor health leading to mortality. These findings suggest that sleep regularity should be monitored and/or adjusted alongside traditional sleep metrics for optimal health and longevity.