Review of sleep disorders

A Review on Automated Sleep Study

In recent years, research on automated sleep analysis has witnessed significant growth, reflecting advancements in understanding sleep patterns and their impact on overall health. This review synthesizes findings from an exhaustive analysis of 87 papers, systematically retrieved from prominent databases such as Google Scholar, PubMed, IEEE Xplore, and ScienceDirect. The selection criteria prioritized studies focusing on methods employed, signal modalities utilized, and machine learning algorithms applied in automated sleep analysis. The overarching goal was to critically evaluate the strengths and weaknesses of the proposed methods, shedding light on the current landscape and future directions in sleep research. An in-depth exploration of the reviewed literature revealed a diverse range of methodologies and machine learning approaches employed in automated sleep studies. Notably, K-Nearest Neighbors (KNN), Ensemble Learning Methods, and Support Vector Machine (SVM) emerged as versatile and potent classifiers, exhibiting high accuracies in various applications. However, challenges such as performance variability and computational demands were observed, necessitating judicious classifier selection based on dataset intricacies. In addition, the integration of traditional feature extraction methods with deep structures and the combination of different deep neural networks were identified as promising strategies to enhance diagnostic accuracy in sleep-related studies. The reviewed literature emphasized the need for adaptive classifiers, cross-modality integration, and collaborative efforts to drive the field toward more accurate, robust, and accessible sleep-related diagnostic solutions. This comprehensive review serves as a solid foundation for researchers and practitioners, providing an organized synthesis of the current state of knowledge in automated sleep analysis. By highlighting the strengths and challenges of various methodologies, this review aims to guide future research toward more effective and nuanced approaches to sleep diagnostics.

Dietary Supplementation with an Extract of Aloysia citrodora (Lemon verbena) Improves Sleep Quality in Healthy Subjects: A Randomized Double-Blind Controlled Study

Seventy-one healthy subjects with sleep disturbances participated in a randomized, double-blind controlled trial in which dietary supplementation with an extract of Aloysia citrodora (lemon verbena) (n = 33) or placebo (n = 38) was administered for 90 days. There were between-group differences in favor of the experimental group in the visual analogue scale (VAS) for sleep quality (6.5 ± 1.6 vs. 5.5 ± 2.1, p = 0.021) as well as in the overall score (5.8 ± 2.4, p = 0.008) and scores for sleep latency (1.6 ± 1.0 vs. 1.9 ± 0.7, p = 0.027) and sleep efficiency (84.5 ± 12.8 vs. 79.8 ± 13.6, p = 0.023) in the Pittsburgh Sleep Quality Index (PSQI). Sleep-related variables (latency, efficiency, wakefulness after sleep onset, awakenings) assessed by actigraphy also showed better scores in the experimental group (p = 0.001). Plasma nocturnal melatonin levels also increased significantly in the experimental group (199.7 ± 135.3 vs. 174.7 ± 115.4 pg/mL, p = 0.048). Changes in anthropometric parameters and physical activity levels were not found. In summary, a dietary supplement of lemon verbena administered for 3 months was associated with a significant improvement in sleep quality as compared with placebo in a population of healthy subjects with sleep problems.

Combined Effects of Ziziphus jujuba, Dimocarpus longan, and Lactuca sativa on Sleep-Related Behaviors through GABAergic Signaling

We aimed to analyze the increase in the sleep-promoting effects based on the mixed ratio of botanical extracts, Ziziphus jujuba seeds, Dimocarpus longan fruits, and Lactuca sativa leaves, using animal models. Behavioral analyses, including an analysis of the total sleep time of Drosophila melanogaster, were conducted to select the optimal mixed ratio of the three botanical extracts. The effects were verified in a caffeine-induced sleepless model, specific neurotransmitter receptor antagonists, and ICR mice. In D. melanogaster exposed to 2.0% of each extract, group behavior was significantly reduced, and the mixed extracts of Z. jujuba, D. longan, and L. sativa (4:1:1 and 1:4:1) significantly increased the total sleep time with individual fruit flies. In the caffeine-induced insomnia model, mixed extracts (4:1:1 and 1:4:1) led to the highest increase in total sleep time. An analysis of locomotor ability revealed a significant reduction in the mobility percentage in the mixed extract groups (0:0:1, 1:0:1, 1:1:1, 4:1:1, and 1:4:1). The administration of Z. jujuba extract and mixed extracts (4:1:1) significantly increased the expression of GABAA-R, whereas the administration of the mixed extracts (4:1:1) and (1:4:1) significantly increased the expression of GABAB-R1 and GABAB-R2, respectively. D. longan extract and the mixed ratio (1:4:1) reduced the subjective nighttime movement and increased the total sleep time in the presence of flumazenil. An analysis of ICR mice indicated that the administration of mixed extracts (4:1:1) significantly increased sleep duration in a dose-dependent manner. These results indicated that the mixed ratio of Z. jujuba, D. longan, and L. sativa extracts, particularly the mixed ratio of 4:1:1, may have sleep-enhancing effects in fruit flies and mice. The study also identified changes in gene expression related to GABA receptors, indicating the potential mechanism for the observed sleep-promoting effects.

Hsp70 ameliorates sleep deprivation-induced anxiety-like behavior and cognitive impairment in mice

BACKGROUND

Many neurobehavioral processes, including psychomotor, cognitive, and affection are negatively impacted by sleep deprivation (SD), which may be harmful to a person's physical and mental health. Heat shock proteins (Hsps) have been demonstrated to play a protective role in a number of neurodegenerative diseases and are essential for maintaining intracellular protein homeostasis, but their roles in SD remain elusive.

METHODS

A mouse SD model was constructed using a modified multi-platform water environment method. The cognitive function was tested by novel object recognition test and Y-maze test, and anxiety-like behaviors were assessed by open field test (OFT). Protein expression was determined by Western blotting assay and ELISA assay.

RESULTS

We found that SD could profoundly enhance anxiety levels and impair cognitive function in mice. SD also reduced the expression levels of p-cAMP-response element binding protein (CREB) and brain-derived neurotrophic factor (BDNF) and increased microglial activation and neuroinflammatory response in the hippocampus of mice. The intranasal injection of human recombinant Hsp70 protein could alleviate SD-induced anxiety and cognitive impairment, as well as restore pCREB and BDNF levels and reduce microglia-induced neuroinflammation in the hippocampus of SD mice.

CONCLUSIONS

Hsp70 treatment might serve as a potential treatment for mitigating SD-related unfavorable symptoms.

An effective hybrid feature selection using entropy weight method for automatic sleep staging

Objective. Sleep staging is the basis for sleep quality assessment and diagnosis of sleep-related disorders. In response to the inadequacy of traditional manual judgement of sleep stages, using machine learning techniques for automatic sleep staging has become a hot topic. To improve the performance of sleep staging, numerous studies have extracted a large number of sleep-related characteristics. However, there are redundant and irrelevant features in the high-dimensional features that reduce the classification accuracy. To address this issue, an effective hybrid feature selection method based on the entropy weight method is proposed in this paper for automatic sleep staging.Approach. Firstly, we preprocess the four modal polysomnography (PSG) signals, including electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG). Secondly, the time domain, frequency domain and nonlinear features are extracted from the preprocessed signals, with a total of 185 features. Then, in order to acquire characteristics of the multi-modal signals that are highly correlated with the sleep stages, the proposed hybrid feature selection method is applied to choose effective features. This method is divided into two stages. In stage I, the entropy weight method is employed to combine two filter methods to build a subset of features. This stage evaluates features based on information theory and distance metrics, which can quickly obtain a subset of features and retain the relevant features. In stage II, Sequential Forward Selection is used to evaluate the subset of features and eliminate redundant features. Further more, to achieve better performance of classification, an ensemble model based on support vector machine, K-nearest neighbor, random forest and multilayer perceptron is finally constructed for classifying sleep stages.Main results. The experiment using the Cyclic Alternating Pattern (CAP) sleep database is performed to assess the performance of the method proposed in this paper. The proposed hybrid feature selection method chooses only 30 features highly correlated to sleep stages. The accuracy, F1 score and Kappa coefficient of 6 class sleep staging reach 88.86%, 83.15% and 0.8531%, respectively.Significance. Experimental results show the effectiveness of the proposed method compared to the existing state-of-the-art studies. It greatly reduces the number of features required while achieving outstanding auto-sleep staging results.

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening

Introduction

Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease.

Methods

The first crucial step in evaluating individuals' quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance.

Results

The performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures.

Discussion

The current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals' sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

The Role of Tryptophan Metabolism in Alzheimer's Disease

The need to identify new potentially druggable biochemical mechanisms for Alzheimer's disease (AD) is an ongoing priority. The therapeutic limitations of amyloid-based approaches are further motivating this search. Amino acid metabolism, particularly tryptophan metabolism, has the potential to emerge as a leading candidate and an alternative exploitable biomolecular target. Multiple avenues support this contention. Tryptophan (trp) and its associated metabolites are able to inhibit various enzymes participating in the biosynthesis of β-amyloid, and one metabolite, 3-hydroxyanthranilate, is able to directly inhibit neurotoxic β-amyloid oligomerization; however, whilst certain trp metabolites are neuroprotectant, other metabolites, such as quinolinic acid, are directly toxic to neurons and may themselves contribute to AD progression. Trp metabolites also have the ability to influence microglia and associated cytokines in order to modulate the neuroinflammatory and neuroimmune factors which trigger pro-inflammatory cytotoxicity in AD. Finally, trp and various metabolites, including melatonin, are regulators of sleep, with disorders of sleep being an important risk factor for the development of AD. Thus, the involvement of trp biochemistry in AD is multifactorial and offers a plethora of druggable targets in the continuing quest for AD therapeutics.

Fear Related to COVID-19, Mental Health Issues, and Predictors of Insomnia among Female Nursing College Students during the Pandemic

Fear of infection has been sparked by the advent of the novel coronavirus disease (COVID-19). Insomnia in college students, especially its correlations and predictions with mental diseases, remains a research concern. Aim: To estimate the prevalence of fear related to COVID-19, depression, anxiety, and insomnia among female nursing college students throughout the pandemic and to determine the predictors of insomnia. Methods: A web-based cross-sectional descriptive study used 145 female nursing college students. Results: Students reported fear related to COVID-19, depression, and anxiety at rates of 79.3%, 30.2%, and 35.2%, respectively. Insomnia disturbed 24.7% of students. Anxiety predicted worsening insomnia in the student (AOR = 1.08, 95% CI: 0.92−0.97, p < 0.001). Fear related to COVID-19 was also a predictor (AOR = 0.96, 95% CI: 1.07−1.21, p < 0.05). Additionally, when depression severity declined, the chance of insomnia improved (AOR = 0.87, 95% CI: 0.85−0.91, p < 0.001). Insomnia was more common in chronically unwell students (AOR = 1.50, 95% CI = 1.01−2.24, p < 0.05). Conclusion: During the COVID-19 pandemic, university students’ mental health should be monitored, and all essential safeguards should be taken, including resource allocation, awareness raising efforts, and the building of a mental health counseling facility.

Multi-Scale Evaluation of Sleep Quality Based on Motion Signal from Unobtrusive Device

Sleep disorders are a growing threat nowadays as they are linked to neurological, cardiovascular and metabolic diseases. The gold standard methodology for sleep study is polysomnography (PSG), an intrusive and onerous technique that can disrupt normal routines. In this perspective, m-Health technologies offer an unobtrusive and rapid solution for home monitoring. We developed a multi-scale method based on motion signal extracted from an unobtrusive device to evaluate sleep behavior. Data used in this study were collected during two different acquisition campaigns by using a Pressure Bed Sensor (PBS). The first one was carried out with 22 subjects for sleep problems, and the second one comprises 11 healthy shift workers. All underwent full PSG and PBS recordings. The algorithm consists of extracting sleep quality and fragmentation indexes correlating to clinical metrics. In particular, the method classifies sleep windows of 1-s of the motion signal into: displacement (DI), quiet sleep (QS), disrupted sleep (DS) and absence from the bed (ABS). QS proved to be positively correlated (0.72±0.014) to Sleep Efficiency (SE) and DS/DI positively correlated (0.85±0.007) to the Apnea-Hypopnea Index (AHI). The work proved to be potentially helpful in the early investigation of sleep in the home environment. The minimized intrusiveness of the device together with a low complexity and good performance might provide valuable indications for the home monitoring of sleep disorders and for subjects’ awareness.

A critical review on clinical evidence of the efficacy of lavender in sleep disorders

Sleep disorders are one of the most prevalent psychiatric diseases. Insomnia is the most common sleep disorder amongst the general population. It is also one of the most frequent complaints in primary healthcare centers. Lavender is called "the broom of the brain" in different oriental traditional medicines. It is one of the most used plants for patients with sleep disorders. This study reviews what is currently known about the use of lavender for sleep disorders in patients with different diseases, from cancers and end-stage renal disease to neurological-psychiatric diseases (e.g., depression, dementia, and autism), respiratory, cardiac, and metabolic diseases. Additionally, its most used administration route is the inhalation of its essential oil (i.e., aromatherapy) alone or in combination by massage. Some limitations of the reviewed literature were discussed briefly. Overall, this critical review provides promising evidence of the lavender efficacy for sleep disorders in a wide variety of populations and diseases. However, further clinical studies with robust design and longer durations of intervention are necessary for more evidence-based judgment on its effect on sleep problems and to investigate its mechanism of action.

Automated Classification of Sleep Stages Using Single-Channel EEG A Machine Learning-Based Method

The main contribution of this paper is to present a novel approach for classifying the sleep stages based on optimal feature selection with ensemble learning stacking model using single-channel EEG signals.To find the suitable features from extracted feature vector, we obtained the ReliefF (ReF), Fisher Score (FS) and Online Stream Feature Selection (OSFS) selection algorithms.The proposed research work was performed on two different subgroups of sleep data of ISRUC-Sleep dataset. The experimental results of the proposed methodology signify that single-channel of EEG signal superior to other machine learning classification models with overall accuracies of 97.93%, 97%, and 95.96% using ISRUC-Sleep subgroup-I (SG-I) data and similarly the proposed model achieved an overall accuracies of 98.16%, 98.78%, and 95.26% using ISRUC-Sleep subgroup-III (SG-III) data with FS, ReF and OSFS respectively.

Sleep disorders in people with type 2 diabetes and associated health outcomes: a review of the literature

Sleep disorders are linked to development of type 2 diabetes and increase the risk of developing diabetes complications. Treating sleep disorders might therefore play an important role in the prevention of diabetes progression. However, the detection and treatment of sleep disorders are not part of standardised care for people with type 2 diabetes. To highlight the importance of sleep disorders in people with type 2 diabetes, we provide a review of the literature on the prevalence of sleep disorders in type 2 diabetes and the association between sleep disorders and health outcomes, such as glycaemic control, microvascular and macrovascular complications, depression, mortality and quality of life. Additionally, we examine the extent to which treating sleep disorders in people with type 2 diabetes improves these health outcomes. We performed a literature search in PubMed from inception until January 2021, using search terms for sleep disorders, type 2 diabetes, prevalence, treatment and health outcomes. Both observational and experimental studies were included in the review. We found that insomnia (39% [95% CI 34, 44]), obstructive sleep apnoea (55-86%) and restless legs syndrome (8-45%) were more prevalent in people with type 2 diabetes, compared with the general population. No studies reported prevalence rates for circadian rhythm sleep-wake disorders, central disorders of hypersomnolence or parasomnias. Additionally, several cross-sectional and prospective studies showed that sleep disorders negatively affect health outcomes in at least one diabetes domain, especially glycaemic control. For example, insomnia is associated with increased HbA_1c levels (2.51 mmol/mol [95% CI 1.1, 4.4]; 0.23% [95% CI 0.1, 0.4]). Finally, randomised controlled trials that investigate the effect of treating sleep disorders in people with type 2 diabetes are scarce, based on a small number of participants and sometimes inconclusive. Conventional therapies such as weight loss, sleep education and cognitive behavioural therapy seem to be effective in improving sleep and health outcomes in people with type 2 diabetes. We conclude that sleep disorders are highly prevalent in people with type 2 diabetes, negatively affecting health outcomes. Since treatment of the sleep disorder could prevent diabetes progression, efforts should be made to diagnose and treat sleep disorders in type 2 diabetes in order to ultimately improve health and therefore quality of life.

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

Mirtazapine reduces susceptibility to hypocapnic central sleep apnea in males with sleep-disordered breathing: a pilot study

Studies in humans and animal models with spinal cord injury (SCI) have demonstrated that medications targeting serotonin receptors may decrease the susceptibility to central sleep-disordered breathing (SDB). We hypothesized that mirtazapine would decrease the propensity to develop hypocapnic central sleep apnea (CSA) during sleep. We performed a single-blind pilot study on a total of 10 men with SDB (7 with chronic SCI and 3 noninjured) aged 52.0 ± 11.2 yr. Participants were randomly assigned to either mirtazapine (15 mg at bedtime) or a placebo for at least 1 wk, followed by a 7-day washout period before crossing over to the other intervention. Split-night studies included polysomnography and induction of hypocapnic CSA using a noninvasive ventilation (NIV) protocol. The primary outcome was CO₂ reserve, defined as the difference between eupneic and end of NIV end-tidal CO₂ ([Formula: see text]) preceding induced hypocapneic CSA. Secondary outcomes included controller gain (CG), other ventilatory parameters, and SDB severity. CG was defined as the ratio of change in minute ventilation (V̇e) between control and hypopnea to the change in CO₂ during sleep. CO₂ reserve was significantly widened on mirtazapine than placebo (-3.8 ± 1.2 vs. -2.0 ± 1.5 mmHg; P = 0.015). CG was significantly decreased on mirtazapine compared with placebo [2.2 ± 0.7 vs. 3.5 ± 1.9 L/(mmHg × min); P = 0.023]. There were no significant differences for other ventilatory parameters assessed or SDB severity between mirtazapine and placebo trials. These findings suggest that the administration of mirtazapine can decrease the susceptibility to central apnea by reducing chemosensitivity and increasing CO₂ reserve; however, considering the lack of changes in apnea-hypopnea index (AHI), further research is required to understand the significance of this finding.NEW & NOTEWORTHY To our knowledge, this research study is novel as it is the first study in humans assessing the effect of mirtazapine on CO₂ reserve and chemosensitivity in individuals with severe sleep-disordered breathing. This is also the first study to determine the potential therapeutic effects of mirtazapine on sleep parameters in individuals with a spinal cord injury.

Psychological aspects of infertility. A systematic review

Objective

Fertility may be defined as a capacity to conceive and produce offspring. Infertility is characterized by failure to establish a clinical pregnancy after 12 months of regular and unprotected sexual intercourse. Infertility concerns an estimated 8–12% of the global population, and is associated with factors including time of unwanted non-conception, age of female partner and number of diseases impacting fertility. Unexplained infertility is described as idiopathic. This study aimed to analyse and evaluate the influence of mental disorders, often considered as reasons for idiopathic infertility, on female and male fertility, including stress, depression, sleep and eating disorders, and addictions.

Methods

This systematic review comprised a search of MEDLINE, Cochrane and PubMed databases for relevant articles that were analysed by two independent reviewers.

Results

A total of 106 articles published between 1955–2019 were included. Mental disorders modify endocrine gland and immune system functioning at both the tissue and cellular level, and are negatively associated with female and male fertility.

Conclusion

Mental disorders may negatively impact female and male fertility. Further studies are required to explain the exact role and contribution of mental disorders to fertility.

Sleep pattern and dozing chance among university students

Background:

Sleep is defined as a naturally occurring state of the body within a relatively inhibited sensory activity, reduced metabolic rate and decreased interaction with the surrounding. Impaired sleep affects students’ productivity, this area is not fully covered in the literature.

Objectives:

To assess sleep patterns and dozing chance among university students.

Methods:

Institutional based cross-sectional study, a sample of 145 male university students from Almaarefa University aged between 19 and 27 years old, chosen randomly. A self-administered questionnaire developed specifically for this study after consulting literature and epidemiologist. It includes data about the Epworth Sleepiness scale and GPA. Data were analyzed using (SPSS, version 22.0) and (P values of ≤0.05) considered significant. The consent was obtained before data collection.

Results:

The majority of respondents (62, 1%) reported sleeping time of 5-8 hours per night. To fall asleep at night 13, 1% of participants indicated needing soporific. Overall, only 36, 6% of students showed good sleep behaviors. Among respondents (44, 1%) had moderate chances of dozing and 2, 8% had high chances of dozing, There was no significant statistical relationship between academic performance and bedtime (P-value = 0,231).

Conclusion:

The majority of respondents had poor sleep quality and moderate to high dosing chance, also, most of the participants go to bed after midnight. In addition, one fifth of participants reported sleeping less than 8 hour per day.

Simultaneous fMRI-EEG-Based Characterisation of NREM Parasomnia Disease: Methods and Limitations

Functional magnetic resonance imaging (fMRI) techniques and electroencephalography (EEG) were used to investigate sleep with a focus on impaired arousal mechanisms in disorders of arousal (DOAs). With a prevalence of 2–4% in adults, DOAs are significant disorders that are currently gaining attention among physicians. The paper describes a simultaneous EEG and fMRI experiment conducted in adult individuals with DOAs (n=10). Both EEG and fMRI data were validated by reproducing well established EEG and fMRI associations. A method for identification of both brain functional areas and EEG rhythms associated with DOAs in shallow sleep was designed. Significant differences between patients and controls were found in delta, theta, and alpha bands during awakening epochs. General linear models of the blood-oxygen-level-dependent signal have shown the secondary visual cortex and dorsal posterior cingulate cortex to be associated with alpha spectral power fluctuations, and the precuneus with delta spectral power fluctuations, specifically in patients and not in controls. Future EEG–fMRI sleep studies should also consider subject comfort as an important aspect in the experimental design.

End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features

Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep disorders. Therefore, the automatic sleep stage classification techniques are needed. However, the previous automatic sleep scoring methods using raw signals are still low classification performance. In this study, we proposed an end-to-end automatic sleep staging framework based on optimal spectral-temporal sleep features using a sleep-edf dataset. The input data were modified using a bandpass filter and then applied to a convolutional neural network model. For five sleep stage classification, the classification performance 85.6% and 91.1% using the raw input data and the proposed input, respectively. This result also shows the highest performance compared to conventional studies using the same dataset. The proposed framework has shown high performance by using optimal features associated with each sleep stage, which may help to find new features in the automatic sleep stage method.Clinical Relevance- The proposed framework would help to diagnose sleep disorders such as insomnia by improving sleep stage classification performance.

Buspirone decreases susceptibility to hypocapnic central sleep apnea in chronic SCI patients

Spinal cord injury (SCI) is a risk factor for central sleep apnea (CSA). Previous studies in animal models with SCI have demonstrated a promising recovery in respiratory and phrenic nerve activity post-injury induced by the systemic and local administration of serotonin receptor agonists such as Buspirone and Trazodone. Human trials must be performed to determine whether individuals with SCI respond similarly. We hypothesized that Buspirone and Trazodone would decrease the propensity to hypocapnic CSA during sleep. We studied eight males with chronic SCI and sleep-disordered breathing (SDB) [age: 48.8 ± 14.2 yr; apnea-hypopnea index (AHI): 44.9 ± 23.1] in a single-blind crossover design. For 13 days, participants were randomly assigned either Buspirone (7.5-15 mg twice daily), Trazodone (100 mg), or a placebo followed by a 14-day washout period before crossing over to the other interventions. Study nights included polysomnography and induction of CSA using a noninvasive ventilation protocol. We assessed indexes of SDB, CO₂ reserve, apneic threshold (AT), controller gain (CG), plant gain (PG), and ventilatory parameters. CO₂ reserve was significantly widened on Buspirone (-3.6 ± 0.9 mmHg) compared with both Trazodone (-2.5 ± 1.0 mmHg, P = 0.009) and placebo (-1.8 ± 1.5 mmHg, P < 0.001) but not on Trazodone vs. placebo (P = 0.061). CG was significantly decreased on Buspirone compared with placebo (1.8 ± 0.4 vs. 4.0 ± 2.0 L/(mmHg·min), P = 0.025) but not on Trazodone compared with placebo (2.5 ± 1.1 vs. 4.0 ± 2.0 L/(mmHg·min); P = 0.065). There were no significant differences for PG, AT, or any SDB indexes (AHI, obstructive apnea index, central apnea index, oxygen desaturation index). The administration of Buspirone decreased the susceptibility to induced hypocapnic central apnea by reducing chemosensitivity and increasing CO₂ reserve in chronic SCI patients.NEW & NOTEWORTHY This research study is novel as it is the first study in a humans that we are aware of that demonstrates the ability of Buspirone to increase CO₂ reserve and hence decrease susceptibility to hypocapnic central apnea in patients with spinal cord injury.

The Effects of Dietary Nutrition on Sleep and Sleep Disorders

Sleep disorder significantly affects the life quality of a large number of people but is still an underrecognized disease. Dietary nutrition is believed to play a significant impact on sleeping wellness. Many nutritional supplements have been used trying to benefit sleep wellness. However, the relationship between nutritional components and sleep is complicated. Nutritional factors vary dramatically with different diet patterns and depend significantly on the digestive and metabiotic functions of each individual. Moreover, nutrition can profoundly affect the hormones and inflammation status which directly or indirectly contribute to insomnia. In this review, we summarized the role of major nutritional factors, carbohydrates, lipids, amino acids, and vitamins on sleep and sleep disorders and discussed the potential mechanisms.

A wake-active locomotion circuit depolarizes a sleep-active neuron to switch on sleep

Sleep-active neurons depolarize during sleep to suppress wakefulness circuits. Wake-active wake-promoting neurons in turn shut down sleep-active neurons, thus forming a bipartite flip-flop switch. However, how sleep is switched on is unclear because it is not known how wakefulness is translated into sleep-active neuron depolarization when the system is set to sleep. Using optogenetics in Caenorhabditis elegans, we solved the presynaptic circuit for depolarization of the sleep-active RIS neuron during developmentally regulated sleep, also known as lethargus. Surprisingly, we found that RIS activation requires neurons that have known roles in wakefulness and locomotion behavior. The RIM interneurons—which are active during and can induce reverse locomotion—play a complex role and can act as inhibitors of RIS when they are strongly depolarized and as activators of RIS when they are modestly depolarized. The PVC command interneurons, which are known to promote forward locomotion during wakefulness, act as major activators of RIS. The properties of these locomotion neurons are modulated during lethargus. The RIMs become less excitable. The PVCs become resistant to inhibition and have an increased capacity to activate RIS. Separate activation of neither the PVCs nor the RIMs appears to be sufficient for sleep induction; instead, our data suggest that they act in concert to activate RIS. Forward and reverse circuit activity is normally mutually exclusive. Our data suggest that RIS may be activated at the transition between forward and reverse locomotion states, perhaps when both forward (PVC) and reverse (including RIM) circuit activity overlap. While RIS is not strongly activated outside of lethargus, altered activity of the locomotion interneurons during lethargus favors strong RIS activation and thus sleep. The control of sleep-active neurons by locomotion circuits suggests that sleep control may have evolved from locomotion control. The flip-flop sleep switch in C. elegans thus requires an additional component, wake-active sleep-promoting neurons that translate wakefulness into the depolarization of a sleep-active neuron when the worm is sleepy. Wake-active sleep-promoting circuits may also be required for sleep state switching in other animals, including in mammals.

This study in nematodes shows that to understand sleep state switching, the flip-flop model for sleep regulation needs to be complemented by additional wake-active sleep-promoting neurons that activate sleep-active sleep-promoting neurons to induce sleep.

Gut Microbiota as an Objective Measurement for Auxiliary Diagnosis of Insomnia Disorder

Insomnia is a type of sleep disorder which is associated with various diseases’ development and progression, such as obesity, type II diabetes and cardiovascular diseases. Recent investigation of the gut-brain axis enhances our understanding of the role of the gut microbiota in brain-related diseases. However, whether the gut microbiota is associated with insomnia remains unknown. In the present investigation, leveraging the 16S rDNA amplicon sequencing of V3-V4 region and the novel bioinformatic analysis, it was demonstrated that between insomnia and healthy populations, the composition, diversity and metabolic function of the gut microbiota are significantly changed. Other than these, redundancy analysis, co-occurrence analysis and PICRUSt underpin the gut taxa composition, signaling pathways, and metabolic functions perturbed by insomnia disorder. Moreover, random forest together with cross-validation identified two signature bacteria, which could be used to distinguish the insomnia patients from the healthy population. Furthermore, based on the relative abundance and clinical sleep parameter, we constructed a prediction model utilizing artificial neural network (ANN) for auxiliary diagnosis of insomnia disorder. Overall, the aforementioned study provides a comprehensive understanding of the link between the gut microbiota and insomnia disorder.

A review of automated sleep stage scoring based on physiological signals for the new millennia

BACKGROUND AND OBJECTIVE

Sleep is an important part of our life. That importance is highlighted by the multitude of health problems which result from sleep disorders. Detecting these sleep disorders requires an accurate interpretation of physiological signals. Prerequisite for this interpretation is an understanding of the way in which sleep stage changes manifest themselves in the signal waveform. With that understanding it is possible to build automated sleep stage scoring systems. Apart from their practical relevance for automating sleep disorder diagnosis, these systems provide a good indication of the amount of sleep stage related information communicated by a specific physiological signal.

METHODS

This article provides a comprehensive review of automated sleep stage scoring systems, which were created since the year 2000. The systems were developed for Electrocardiogram (ECG), Electroencephalogram (EEG), Electrooculogram (EOG), and a combination of signals.

RESULTS

Our review shows that all of these signals contain information for sleep stage scoring.

CONCLUSIONS

The result is important, because it allows us to shift our research focus away from information extraction methods to systemic improvements, such as patient comfort, redundancy, safety and cost.

BaSIQS - basic scale on insomnia complaints and quality of sleep: reliability, norms, validity, and accuracy studies, based on clinical and community samples

This research focused on the Basic Scale on Insomnia Symptoms and Quality of Sleep (BaSIQS), formerly validated in undergraduates using the Pittsburgh Sleep Quality Index (PSQI), and aimed to expand internal consistency analysis, examine thoroughly its validity, and determine its clinical accuracy. Considering objective and subjective measures, recruiting non-clinical and clinical samples, this research implemented a comprehensive approach to examine convergent and discriminant validity, confirmatory factor analyses, and the BaSIQS sensitivity and specificity. The BaSIQS was filled out along with the Insomnia Severity Index (ISI), questions on sleep-wake schedules, Composite Scale of Morningness (CSM) and Brief Symptom Inventory-18 (BSI-18) by 1198 adults, 18-64 years old, plus another 30 who wore actimeters, recruited in community settings. A clinical group of 30 chronic insomnia disorder patients also participated. Cronbach alpha coefficient was 0.80. A two-factor structure was confirmed. The association between BaSIQS and ISI was large, whereas actigraphy correlations were medium or small. Medium to non-significant correlations were found concerning conceptually different self-report measures. Comparing the clinic and control groups, the former showed poorer sleep, with a large effect size. Receiver operating characteristic analysis revealed an area under curve = 0.9, and an optimal cut-off score >15. In conclusion, results on reliability, validity, and accuracy provide support to the utility of the BaSIQS both in community and clinical settings, for research and practical purposes.

Nocturnal Activity Is Not Affected by a Long-Duration, Low-Intensity Single Exercise Bout

The aim of the current study was to examine whether prolonged low-intensity aerobic exercise could affect nocturnal activity in healthy individuals. Twenty-one healthy adults (24 ± 3.7 years; 9 females) were enrolled in this study. All participants participated in a 3-h low-intensity walking exercise protocol. Standard biochemical indices were assessed before the exercise protocol and at 72 h. Nocturnal activity and various indices of health were recorded for five consecutive days. The score of muscle pain peaked the night after the exercise protocol (p < 0.05) and returned to baseline two days after. No statistical differences were found in any of the parameters examined, including nocturnal activity. Prolonged low-intensity exercise does not affect nocturnal activity. The anecdotal reports suggesting that exercise or/and physical activity could worsen symptoms of motor restlessness during sleep in sleep disorders, such as restless legs syndrome and periodic limb movements, are not supported by this study. However, these findings need to be verified in clinical populations, as well as by using protocols with different forms of exercise.

A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals

Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.

Monitoring healthy and disturbed sleep through smartphone applications: a review of experimental evidence

Smartphone applications are considered as the prime candidate for the purposes of large-scale, low-cost and long-term sleep monitoring. How reliable and scientifically grounded is smartphone-based assessment of healthy and disturbed sleep remains a key issue in this direction. Here we offer a review of validation studies of sleep applications to the aim of providing some guidance in terms of their reliability to assess sleep in healthy and clinical populations, and stimulating further examination of their potential for clinical use and improved sleep hygiene. Electronic literature review was conducted on Pubmed. Eleven validation studies published since 2012 were identified, evaluating smartphone applications' performance compared to standard methods of sleep assessment in healthy and clinical samples. Studies with healthy populations show that most sleep applications meet or exceed accuracy levels of wrist-based actigraphy in sleep-wake cycle discrimination, whereas performance levels drop in individuals with low sleep efficiency (SE) and in clinical populations, mirroring actigraphy results. Poor correlation with polysomnography (PSG) sleep sub-stages is reported by most accelerometer-based apps. However, multiple parameter-based applications (i.e., EarlySense, SleepAp) showed good capability in detection of sleep-wake stages and sleep-related breathing disorders (SRBD) like obstructive sleep apnea (OSA) respectively with values similar to PSG. While the reviewed evidence suggests a potential role of smartphone sleep applications in pre-screening of SRBD, more experimental studies are warranted to assess their reliability in sleep-wake detection particularly. Apps' utility in post treatment follow-up at home or as an adjunct to the sleep diary in clinical setting is also stressed.

Association Between Employee Sleep With Workplace Health and Economic Outcomes

OBJECTIVE

Poor sleep can impact occupational functioning. The current study examines health risks, medical conditions, and workplace economic outcomes associated with self-reported hours of sleep among employees.

METHODS

Employees of a global financial services corporation were categorized on the basis of their self-reported average hours of sleep. Differences in health care costs, productivity measures, health risks, and medical conditions were analyzed by hours of sleep while controlling for confounding variables.

RESULTS

A strong U-shaped relationship between health care costs, short-term disability, absenteeism, and presenteeism (on-the-job work loss) and the hours of sleep was found among employees. The nadir of the "U" occurs for 7 or 8 hours of sleep per night.

CONCLUSIONS

Worksite wellness programs often address health risks and medical conditions and may benefit from incorporating sleep education.

TfAP-2 is required for night sleep in Drosophila

BACKGROUND

The AP-2 transcription factor APTF-1 is crucially required for developmentally controlled sleep behavior in Caenorhabditis elegans larvae. Its human ortholog, TFAP-2beta, causes Char disease and has also been linked to sleep disorders. These data suggest that AP-2 transcription factors may be highly conserved regulators of various types of sleep behavior. Here, we tested the idea that AP-2 controls adult sleep in Drosophila.

RESULTS

Drosophila has one AP-2 ortholog called TfAP-2, which is essential for viability. To investigate its potential role in sleep behavior and neural development, we specifically downregulated TfAP-2 in the nervous system. We found that neuronal TfAP-2 knockdown almost completely abolished night sleep but did not affect day sleep. TfAP-2 insufficiency affected nervous system development. Conditional TfAP-2 knockdown in the adult also produced a modest sleep phenotype, suggesting that TfAP-2 acts both in larval as well as in differentiated neurons.

CONCLUSIONS

Thus, our results show that AP-2 transcription factors are highly conserved regulators of development and sleep.

A practical guide to evaluating sleep disturbance in concussion patients

Purpose of review

To provide an introduction and review of sleep metrics appropriate for use with an outpatient concussed population.

Recent findings

Although sleep issues are frequently identified in concussion, sleep disturbance is rarely assessed in outpatient settings. Given that sleep disturbance may be indicative of, or contribute to, delayed neurocognitive recovery, measurement tools for sleep, rest, and activity behavior may be of both practical and research utility.

Summary

Because sleep disturbance symptoms may vary between or within an individual throughout the course of recovery, it is recommended that sleep be measured at regular intervals over the entirety of recovery. Included is a discussion on how to select appropriate measures based on patient symptomology in addition to common practical concerns. Additional clinical considerations, a review of traditional pencil and paper methods of continuous sleep behavior monitoring, as well as technologies for measuring sleep and activity behavior are also included.

Sleep-active neuron specification and sleep induction require FLP-11 neuropeptides to systemically induce sleep

Sleep is an essential behavioral state. It is induced by conserved sleep-active neurons that express GABA. However, little is known about how sleep neuron function is determined and how sleep neurons change physiology and behavior systemically. Here, we investigated sleep in Caenorhabditis elegans, which is induced by the single sleep-active neuron RIS. We found that the transcription factor LIM-6, which specifies GABAergic function, in parallel determines sleep neuron function through the expression of APTF-1, which specifies the expression of FLP-11 neuropeptides. Surprisingly FLP-11, and not GABA, is the major component that determines the sleep-promoting function of RIS. FLP-11 is constantly expressed in RIS. At sleep onset RIS depolarizes and releases FLP-11 to induce a systemic sleep state.

What is known about the experiences of using CPAP for OSA from the users' perspective? A systematic integrative literature review

Economic, social and personal costs of untreated obstructive sleep apnoea (OSA) are high. Continuous positive airway pressure (CPAP) is recommended and cost effective. Increasing OSA prevalence may accompany predicted globally increasing obesity.

OBJECTIVE

To synthesise international evidence regarding personal experiences using CPAP for OSA.

METHODS

A systematic integrative literature review was conducted and quality assessment criteria applied.

RESULTS

22, of 538, identified papers met inclusion criteria. Thematic analysis identified three themes: 1) users' beliefs about CPAP influence users' experiences of CPAP; 2) CPAP users are primed to reflect negatively on experiences of CPAP; and 3) spouse and family influence users' experiences of CPAP. Personality and attitude impact expectations about CPAP prior to use, whilst engagement of spouse and family also influence experiences. Analysis highlighted that users' reporting of CPAP experiences is constrained by investigator defined assessment methods. Overall, research relating to experiences using CPAP is limited.

CONCLUSION

Users' perspectives of CPAP are constrained by researchers' concern with non-compliance. Typically experiences are not defined by the user, but from an 'expert' healthcare perspective, using words which frame CPAP as problematic. Family and social support is a significant, but neglected area of experiencing CPAP warranting further investigation. More information from users is required to determine how CPAP can be managed successfully.

Effects of exercise and diet interventions on obesity-related sleep disorders in men: study protocol for a randomized controlled trial

BACKGROUND

Sleep is essential for normal and healthy living. Lack of good quality sleep affects physical, mental and emotional functions. Currently, the treatments of obesity-related sleep disorders focus more on suppressing sleep-related symptoms pharmaceutically and are often accompanied by side effects. Thus, there is urgent need for alternative ways to combat chronic sleep disorders. This study will investigate underlying mechanisms of the effects of exercise and diet intervention on obesity-related sleep disorders, the role of gut microbiota in relation to poor quality of sleep and day-time sleepiness, as well as the levels of hormones responsible for sleep-wake cycle regulation.

METHODS/DESIGN

Participants consist of 330 (target sample) Finnish men aged 30 to 65 years. Among them, we attempt to randomize 180 (target sample) with sleep disorders into exercise and diet intervention. After screening and physician examination, 101 men with sleep disorders are included and are randomly assigned into three groups: exercise (n = 33), diet (n = 35), and control (n = 33). In addition, we attempt to recruit a target number of 150 healthy men without sleep disorders as the reference group. The exercise group undergoes a six-month individualized progressive aerobic exercise program based on initial fitness level. The diet group follows a six month specific individualized diet program. The control group and reference group are asked to maintain their normal activity and diet during intervention. Measurements are taken before and after the intervention. Primary outcomes include objective sleep measurements by polysomnography and a home-based non-contact sleep monitoring system, and subjective sleep evaluation by questionnaires. Secondary outcome measures include anthropometry, body composition, fitness, sleep disorder-related lifestyle risk factors, composition of gut microbiota and adipose tissue metabolism, as well as specific hormone and neurotranmitter levels and inflammatory biomarkers from venous blood samples.

DISCUSSION

It is expected that the improvement of sleep quality after exercise and diet intervention will be evident both in subjective and objective measures of quality of sleep. Additionally, the change of sleep quality induced by exercise and diet intervention is expected to be related to the changes in specific hormones and inflammatory biomarkers, and in the composition of gut microbiota.

A Pan-Canadian practice guideline: prevention, screening, assessment, and treatment of sleep disturbances in adults with cancer

PURPOSE

This study aims to provide recommendations on the optimal strategies and interventions for the prevention, screening, assessment, and management of cancer-related sleep disturbance (insomnia and insomnia syndrome) in adult cancer populations.

METHODS

A systematic search of the published health literature was conducted to identify randomized controlled trials, clinical practice guidelines, systematic reviews, and other guidance documents. The Sleep Disturbance Expert Panel [comprised of nurses, psychologists, primary care physicians, oncologists, physicians specialized in sleep disturbances, researchers, and guideline methodologists] reviewed, discussed, and approved the final version of the guideline. Health care professionals across Canada were asked to provide feedback through an external review process.

RESULTS

Three clinical practice guidelines and 12 randomized controlled trials were identified as the evidence base. Overall, despite the paucity of evidence, the evidence and expert consensus suggest that it is important to screen and assess adult cancer patients for sleep disturbances using standardized screening tools on a routine basis. While prevention of sleep disturbance is the desired objective, cognitive behavioral therapies are effective in improving sleep outcomes. As part of the external review with 16 health care providers, 81 % indicated that they agreed with the recommendations as written.

CONCLUSIONS

Sleep difficulty is a prevalent problem in cancer populations that needs greater recognition by health professionals. Prevention, screening, assessment, and treatment strategies supported by the best available evidence are critical. Recommendations and care path algorithms for practice are offered.

The relationship of primary health care use with persistence of insomnia: a prospective cohort study

Background

Prevalence of insomnia symptoms in the general population is high. Insomnia is linked with high health care use and within primary care there are a number of treatment options available. The objective of this study was to determine the association of persistence and remission of insomnia with primary health care using a longitudinal study.

Methods

A postal survey of registered adult (over 18 years) populations of five UK general practices, repeated after 1 year, linked to primary care records. Baseline survey responders were assessed for persistence of insomnia symptoms at 12 months. The association of primary care consultation or prescription for any mood disorder (defined as anxiety, depression, stress, neurosis, or insomnia) in the 12 months between baseline and follow-up surveys with persistence of insomnia was determined.

Results

474 participants reporting insomnia symptoms at baseline were followed up at 12 months. 131(28%) consulted for mood problem(s) or received a relevant prescription. Of these 100 (76%) still had insomnia symptoms at one year, compared with 227 (66%) of those with no contact with primary care for this condition (OR 1.37; 95% CI 0.83, 2.27). Prescription of hypnotics showed some evidence of association with persistence of insomnia at follow-up (OR 3.18; 95% CI 0.93, 10.92).

Conclusion

Insomniacs continue to have problems regardless of whether or not they have consulted their primary care clinician or received a prescription for medication over the year. Hypnotics may be associated with persistence of insomnia. Further research is needed to determine more effective methods of identifying and managing insomnia in primary care. There may however be a group who have unmet need such as depression who would benefit from seeking primary health care.

Impact of obstructive sleep apnea on cognitive performance

OBJECTIVE: To evaluate the impact of obstructive sleep apnea (OSA) on cognition. METHOD: We compared the performance of 17 patients with polysomnographic diagnosis of OSA in brief cognitive tests to that of 20 healthy controls, matched for age and education. The testing battery included the Mini-Mental State Examination (MMSE), Brief Cognitive Screening Battery (BCSB), Digit-Symbol (DS) and Phonemic Verbal Fluency (FAS). Anthropometric measures and scores from the Epworth Sleepiness Scale were also recorded. RESULTS: OSA patients performed significantly worse than controls in the MMSE, in memory items from the BCSB, in DS and also in FAS. OSA patients also exhibited higher body mass index, increased neck circumference and higher scores in Epworth Sleepiness Scale than controls. CONCLUSION: OSA significantly impairs cognitive performance, especially within the domains of attention, memory and executive functioning. These deficits may be detected by brief and easy-to-administer cognitive tests.

Problems in functioning from the patient perspective using the International Classification of Functioning, Disability and Health (ICF) as a reference

We conducted a qualitative, multicenter study using a focus group design to explore the lived experiences of persons with any kind of primary sleep disorder with regard to functioning and contextual factors using six open-ended questions related to the International Classification of Functioning, Disability and Health (ICF) components. We classified the results using the ICF as a frame of reference. We identified the meaningful concepts within the transcribed data and then linked them to ICF categories according to established linking rules. The six focus groups with 27 participants yielded a total of 6986 relevant concepts, which were linked to a total of 168 different second-level ICF categories. From the patient perspective, the ICF components: (1) Body Functions; (2) Activities & Participation; and (3) Environmental Factors were equally represented; while (4) Body Structures appeared poignantly less frequently. Out of the total number of concepts, 1843 concepts (26%) were assigned to the ICF component Personal Factors, which is not yet classified but could indicate important aspects of resource management and strategy development of those who have a sleep disorder. Therefore, treatment of patients with sleep disorders must not be limited to anatomical and (patho-)physiological changes, but should also consider a more comprehensive view that includes patient's demands, strategies and resources in daily life and the contextual circumstances surrounding the individual.

Wireless portable electrocardiogram and a tri-axis accelerometer implementation and application on sleep activity monitoring

Night-to-night variability of sleep activity requires more home-based portable sleep monitoring instead of clinical polysomnography examination in the laboratory. In this article, a wireless sleep activity monitoring system is described. The system is light and small for the user. Sleep postures, such as supine or left/right side, were observed by a signal from a tri-axis accelerometer. An overnight electrocardiogram was also recorded with a single lead. Using an MSP430 as microcontroller, both physiological signals were transmitted by a Bluetooth chip. A Labview-based interface demonstrated the recorded signal and sleep posture. Three nights of sleep recordings were used to examine night-to-night variability. The proposed system can record overnight heart rate. Results show that sleep posture and posture change can be precisely detected via tri-axis accelerometer information. There is no significant difference within subject data sets, but there are statistically significant differences among subjects, both for heart rate and for sleep posture distribution. The wireless transmission range is also sufficient for home-based users.

Sleep disturbances in dementia

Approximately one quarter of adults with dementia experience sleep disturbances. The purpose of this article is to (a) describe and define sleep disturbances in individuals with dementia, (b) describe techniques to assess for sleep disturbances in individuals with dementia, and (c) provide nursing interventions to improve sleep in this patient population. Typical presentations of sleep disturbances in individuals with dementia are described, along with medications that may interfere with sleep. Suggestions for nursing measures that can be implemented to enhance sleep are also presented. Nurses have numerous nonpharmacological options to assist with the regulation of sleep-wake rhythms in individuals with dementia.