Treatment Of Sleep-related Disorders Research Articles

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals.

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet’s cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen’s Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen’s Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

0760 SUVN-G3031, A Potent And Selective Histamine H3 Receptor Inverse Agonist: Safety, Tolerability And Pharmacokinetics Following Single And Multiple Ascending Doses In Healthy Adult Subjects

Abstract Introduction SUVN-G3031 is a potent and selective histamine H3 receptor inverse agonist currently being developed for the treatment of narcolepsy. SUVN-G3031 produced robust wake promoting and anticataplectic effects in animal model relevant to the disease. This supports its therapeutic utility in the treatment of sleep related disorders like narcolepsy with and without cataplexy. Methods Two Phase 1 studies were conducted to assess safety, tolerability and pharmacokinetics (PK) of SUVN-G3031. In the first study, single ascending doses of 0.1 mg to 20 mg SUVN-G3031 were administered to healthy subjects. For multiple ascending dose cohorts, doses of 1 mg to 6 mg were administered for 14 days. In the second Phase 1 study, effects of food, gender and age on the PK of SUVN-G3031 were assessed. Results SUVN-G3031 absorbed rapidly following single oral administration and the exposures (Cmax and AUC) were dose proportional at the tested doses between 0.1 mg to 20 mg. SUVN-G3031 attained steady state on day six and achieved projected efficacy concentrations following repeated administrations. Food, gender and age had no effect on pharmacokinetics of SUVN-G3031. SUVN-G3031 was well tolerated up to 20 mg/ day single dose and 6 mg repeated dose in healthy adult subjects. There were no serious adverse events reported by any subject during Phase 1 studies. Conclusion SUVN-G3031 was well tolerated in humans with adequate plasma exposures for efficacy and has favorable pharmacokinetics suitable for once a day oral administration. SUVN-G3031 is currently being evaluated in a Phase 2 study as monotherapy for the treatment of narcolepsy with and without cataplexy (ClinicalTrials.gov Identifier: NCT04072380). Support None

Automatic Sleep Stage Classification With Single Channel EEG Signal Based on Two-Layer Stacked Ensemble Model

Sleep stage classification, including wakefulness (W), rapid eye movement (REM), and non- rapid eye movement (NREM) which includes three sleep stages that describe the depth of sleep, is one of the most critical steps in effective diagnosis and treatment of sleep-related disorders. Clinically, sleep staging is performed by domain experts through visual inspection of polysomnography (PSG) recordings, which is time-consuming, labor-intensive and often subjective in nature. Therefore, this study develops an automatic sleep staging system, which uses single channel electroencephalogram (EEG) signal, for convenience of wearing and less interference in the sleep, to do automatic identification of various sleep stages. To achieve the automatic sleep staging system, this study proposes a two-layer stacked ensemble model, which combines the advantages of random forest (RF) and LightGBM (LGB), where RF focuses on reducing the variance of the proposed model while LGB focuses on reducing the bias of the proposed model. Particularly, the proposed model introduces a class balance strategy to improve the N1 stage recognition rate. In order to evaluate the performance of the proposed model, experiments are performed on two datasets, including Sleep-EDF database (SEDFDB) and Sleep-EDF Expanded database (SEDFEDB). In the SEDFDB, the overall accuracy (ACC), weight F1-score (WF1), Cohen’s Kappa coefficient (Kappa), sensitivity of N1 (SEN-N1) obtained by proposed model are 91.2%, 0.916, 0.864 and 72.52% respectively using subject-non-independent test (SNT). In parallel, the ACC, WF1, Kappa, SEN-N1 obtained by proposed model are 82.4%, 0.751, 0.719 and 27.15% respectively using subject-independent test (SIT). Experimental results show that the performance of the proposed model are competitive with the state-of-the-art methods and results, and the recognition rate of N1 stage is significantly improved. Moreover, in the SEDFEDB, the experimental results indicate the robustness and generality of the proposed model.

Automatic sleep stages classification using optimize flexible analytic wavelet transform

Sleep stages classification avails the diagnosis and treatment of sleep-related disorders. The traditional visual inspection methods used by sleep-experts are time-consuming and error-prone. This framework proposes, an automatic sleep stages classification method based on optimize flexible analytic wavelet transform (OFAWT) for electroencephalogram (EEG) signals. In OFAWT, the parametric optimization is performed to obtain the most appropriate basis for the representation of EEG signals. The OFAWT parameters are selected by solving inequality constraints problem using the genetic algorithm. OFAWT decomposes EEG signal into band-limited basis or sub-bands (SBs). Time domain measures of SBs are used as features for the sleep stages EEG signals. The statistical significance of extracted features is assessed by multiple-comparison post hoc analysis of Kruskal–Wallis test, which ensures that reported features are statistically significant for the discrimination of sleep stages. The SB-wise features set is tested through the variants of decision tree, discriminant analysis, k-nearest neighbor, and ensemble classifiers for sleep stages classification. The ensemble classification model bagged-tree yields better classification accuracies for the classification of six to two sleep stages 96.03%, 96.39%, 96.48%, 97.56%, and 99.36%, respectively as compared to other existing methods.

A Novel Multi-Class EEG-Based Sleep Stage Classification System.

Sleep stage classification is one of the most critical steps in effective diagnosis and the treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time-consuming and burdensome task. A computer-assisted sleep stage classification system is thus essential for both sleep-related disorders diagnosis and sleep monitoring. In this paper, we propose a system to classify the wake and sleep stages with high rates of sensitivity and specificity. The EEG signals of 25 subjects with suspected sleep-disordered breathing, and the EEG signals of 20 healthy subjects from three data sets are used. Every EEG epoch is decomposed into eight subband epochs each of which has a frequency band pertaining to one EEG rhythm (i.e., delta, theta, alpha, sigma, beta 1, beta 2, gamma 1, or gamma 2). Thirteen features are extracted from each subband epoch. Therefore, 104 features are totally obtained for every EEG epoch. The Kruskal-Wallis test is used to examine the significance of the features. Non-significant features are discarded. The minimal-redundancy-maximal-relevance feature selection algorithm is then used to eliminate redundant and irrelevant features. The features selected are classified by a random forest classifier. To set the system parameters and to evaluate the system performance, nested 5-fold cross-validation and subject cross-validation are performed. The performance of our proposed system is evaluated for different multi-class classification problems. The minimum overall accuracy rates obtained are 95.31% and 86.64% for nested 5-fold and subject cross-validation, respectively. The system performance is promising in terms of the accuracy, sensitivity, and specificity rates compared with the ones of the state-of-the-art systems. The proposed system can be used in health care applications with the aim of improving sleep stage classification.

Sleep Quality Study Among Undergraduate Students

SESSION TITLE: Sleep 1 SESSION TYPE: Original Investigation Poster PRESENTED ON: Wednesday, November 1, 2017 at 01:30 PM - 02:30 PM PURPOSE: The purpose of the study was to conduct a questionnaire based research study examining excessive daytime sleepiness and quality of sleep in undergraduate college students. In the last few years, there has been a growing interest in sleep due to the recognition that sleepiness, loss of focus and fatigue are becoming endemic in the general population. METHODS: The quality of sleep was investigated among 100 undergraduate students at the University of South Florida using the Epsworth Sleepiness Scale (ESS) and the Pittsburgh Sleep Quality Index (PSQI) Questionnaire. The Fisher Extract Test was used to determine significance between the various groups observed. RESULTS: A significantly greater number of female students, compared to male students, scored 10 or higher on the ESS (56% versus 26% of males; (p= <0.05). There was a higher percentage of poor scores in quality of sleep (PSQI) among females compared to males but it was not statistically significant. (87% versus 72%; p=0.08). Third year students in general had higher ESS scores compared to the rest of the students( 52% versus 37%, 32%, 25% ; p = 0.30). PSQI scores were elevated among the students of all years (ranging from 72% to 87%; p =0.45), but the PSQI and ESS scores were not observed to be statistically significant among the groups divided by year. CONCLUSIONS: Daytime sleepiness was higher among the female students of all the years. PSQI score was also greater among the female students, but not statistically significant. A high percentage of poor quality of sleep was noted among all the undergraduate students with a majority of them having PSQI scores of 5 and higher. Sleep education may have beneficial effects on the quality of sleep among the college students. CLINICAL IMPLICATIONS: Sleep education should be incorporated into the curriculum of undergraduate student orientation programs so that students will understand the importance of sleep. Sleep awareness will help in early detection and treatment of sleep related disorders. DISCLOSURE: The following authors have nothing to disclose: Steven Kanaparthi, Rachel Moodey No Product/Research Disclosure Information

Sleep-inducing effect of substance P-cholera toxin A subunit in mice.

Evidence indicates that the neuropeptide substance P (SP) can act through neurokinin receptors to alter sleep and/or non-rapid eye movement (NREM) sleep slow-wave activity. Consequently, drugs acting on SP receptors could potentially be used as a novel treatment for sleep-related disorders. In the present study, we used SP conjugated with cholera toxin A subunit (SP-CTA), which enhances its duration of activity on SP receptor-expressing cells, to determine the effects of selectively activating SP receptor-expressing brain cells on sleep regulation in mice. Herein, we found that intracerebroventricular administration of SP-CTA enhanced amounts of NREM sleep which was highly fragmented. This result suggests that the activation of SP receptor-expressing cells in the brain can produce not only arousal effects as shown in previous studies but also sleep-inducing effects.

Creatine supplementation reduces sleep need and homeostatic sleep pressure in rats.

Sleep has been postulated to promote brain energy restoration. It is as yet unknown if increasing the energy availability within the brain reduces sleep need. The guanidine amino acid creatine (Cr) is a well-known energy booster in cellular energy homeostasis. Oral Cr-monohydrate supplementation (CS) increases exercise performance and has been shown to have substantial effects on cognitive performance, neuroprotection and circadian rhythms. The effect of CS on cellular high-energy molecules and sleep-wake behaviour is unclear. Here, we examined the sleep-wake behaviour and brain energy metabolism before and after 4-week-long oral administration of CS in the rat. CS decreased total sleep time and non-rapid eye movement (NREM) sleep significantly during the light (inactive) but not during the dark (active) period. NREM sleep and NREM delta activity were decreased significantly in CS rats after 6h of sleep deprivation. Biochemical analysis of brain energy metabolites showed a tendency to increase in phosphocreatine after CS, while cellular adenosine triphosphate (ATP) level decreased. Microdialysis analysis showed that the sleep deprivation-induced increase in extracellular adenosine was attenuated after CS. These results suggest that CS reduces sleep need and homeostatic sleep pressure in rats, thereby indicating its potential in the treatment of sleep-related disorders.

Sleep-related breathing disorders and non-invasive ventilation

Non-invasive mechanical ventilation (NPPV) was originally used in patients with acute respiratory impairment or exacerbations of chronic respiratory diseases, as an alternative to the endotracheal tube. Over the last thirty years NPPV has been also used at night in patients with stable chronic lung disease such as obstructive sleep apnea, the overlap syndrome (chronic obstructive pulmonary disease and obstructive sleep apnea), neuromuscular disorders, obesity-hypoventilation syndrome, and in other conditions such as sleep disorders associated with congestive heart failure (Cheyne-Stokes respiration). In this no-systematic review we discuss the different types of NPPV, the specific conditions in which they can be used and the indications, recommendations and evidence supporting the efficacy of NPPV. Optimizing patient acceptance and adherence to non-invasive ventilation treatment is challenging. The treatment of sleep-related disorders is a life-threatening condition. The optimal level of treatment should be determined in a sleep laboratory. Side effects directly affecting the patient’s adherence to treatment are known. The most common are nasopharyngeal symptoms including increased congestion and rhinorrhea; these effects are related to reduced humidity of inspired gas. Humidification of delivered gas may improve these symptoms.

Noninvasive ventilation in the treatment of sleep-related breathing disorders: concise clinical review

Non-invasive mechanical ventilation (NIV) was originally used in patients with acute respiratory compromises or exacerbations of chronic respiratory diseases, as an alternative to the endotracheal tube. Over the last thirty years NPPV has been also used during the night in patients with stable chronic lung disease such as obstructive sleep apnea, the overlap syndrome (COPD and obstructive sleep apnea), neuromuscular disorders, obesity-hypoventilation syndrome, and in other conditions such as sleep disorders associated with congestive heart failure (Cheyne-Stokes respiration). In this review we discuss the different types of NPPV, the specific conditions in which they can be used and the indications, recommendations and evidence supporting the efficacy of NIV. Obstructive sleep apnea syndrome (OSA) is characterized commonly by instability of upper airway during sleep, reduction or elimination of airflow, daytime hypersomnolence, sleep disruption. It is a risk factor for cardiovascular and cerebrovascular disorders including hypertension, myocardial infarction and stroke. Optimizing patient acceptance and adherence to non-invasive ventilation treatment is challenging. The treatment of sleep-related disorders is a life-threatening condition. The optimal level of treatment should be determinate in a sleep laboratory. Side effects directly affecting the patient’s adherence to treatment are known. The most common are nasopharyngeal symptoms including increased congestion and rhinorrhea; these effects are related to reduced humidity of inspired gas. Humidification of delivered gas may improve these symptoms. Sleep specialists should review the results of objective testing with the patient. Education of the patient concerning the nature of the disorder and treatment options is important. General education on the impact of weight loss, sleep position, alcohol avoidance, risk factor modification and medication effects should be discussed. The patient should be counseled on the risks and management of drowsy driving. Patient education should optimally be delivered as a part of a multidisciplinary chronic disease management team.

Non-Invasive Ventilation in the Treatment of Sleep-Related Breathing Disorders: Concise Clinical Review

Non-invasive mechanical ventilation (NIV) was originally used in patients with acute respiratory compromises or exacerbations of chronic respiratory diseases, as an alternative to the endotracheal tube. Over the last thirty years NPPV has been also used during the night in patients with stable chronic lung disease such as obstructive sleep apnea, the overlap syndrome (COPD and obstructive sleep apnea), neuromuscular disorders, obesity-hypoventilation syndrome, and in other conditions such as sleep disorders associated with congestive heart failure (Cheyne-Stokes respiration). In this review we discuss the different types of NPPV, the specific conditions in which they can be used and the indications, recommendations and evidence supporting the efficacy of NIV. Obstructive sleep apnea syndrome (OSA) is characterized commonly by instability of upper airway during sleep, reduction or elimination of airflow, daytime hypersomnolence, sleep disruption. It is a risk factor for cardiovascular and cerebrovascular disorders including hypertension, myocardial infarction and stroke. Optimizing patient acceptance and adherence to non-invasive ventilation treatment is challenging. The treatment of sleep-related disorders is a life-threatening condition. The optimal level of treatment should be determinate in a sleep laboratory. Side effects directly affecting the patient’s adherence to treatment are known. The most common are nasopharyngeal symptoms including increased congestion and rhinorrhea; these effects are related to reduced humidity of inspired gas. Humidification of delivered gas may improve these symptoms. Sleep specialists should review the results of objective testing with the patient. Education of the patient concerning the nature of the disorder and treatment options is important. General education on the impact of weight loss, sleep position, alcohol avoidance, risk factor modification and medication effects should be discussed. The patient should be counseled on the risks and management of drowsy driving. Patient education should optimally be delivered as a part of a multidisciplinary chronic disease management team.

Snoring and Obstructive Sleep Apnea from a Dental Perspective

Proper diagnosis and treatment of sleep-related disorders are best handled via a team approach. This team may include a general dentist treating in conjunction with other sleep specialists. However, to provide care, dentists must have a basic understanding of sleep disorders. This paper provides a dental overview of sleep-related breathing disorders, including key definitions, an outline of a diagnostic protocol, a discussion of the factors involved in decision-making, and a summary of the wide variety of treatment modalities.