Early Detection Of Sleep Disorders Research Articles

Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency-modulated continuous-wave radar.

Given the significant impact of sleep on overall health, radar technology offers a promising, non-invasive, and cost-effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)-based classification. In this study, we employed an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model to accurately predict sleep stages using 60 GHz frequency-modulated continuous-wave (FMCW) radar. Our dataset comprised 78 participants from an ongoing obstructive sleep apnea (OSA) cohort, recruited between July 2021 and November 2022, who underwent overnight polysomnography alongside radar sensor monitoring. The dataset encompasses comprehensive polysomnography recordings, spanning both sleep and wakefulness states. The predictions achieved a Cohen's kappa coefficient of 0.746 and an overall accuracy of 85.2% in classifying wakefulness, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep (N1 + N2 + N3). The results demonstrated that the models incorporating both Radar 1 and Radar 2 data consistently outperformed those using only Radar 1 data, indicating the potential benefits of utilising multiple radars for sleep stage classification. Although the performance of the models tended to decline with increasing OSA severity, the addition of Radar 2 data notably improved the classification accuracy. These findings demonstrate the potential of radar technology as a valuable screening tool for sleep stage classification.

Anxiety and sleep disorders

Anxiety disorders are widespread among patients of primary care physicians and are the cause of increased morbidity and access to healthcare. Anxiety is a reaction of the body to the effects of stress and is manifested by the development of psychovegetative syndrome. In the general population, 5 to 7% of people have clinically significant anxiety, and in the practice of primary care physicians, these disorders are detected in every fourth patient. Anxiety disorder contributes to the development of psychogenic somatic pathology and/or negatively affects the course of background disease. Most patients with anxiety disorders suffer from various sleep disorders. In general practice, the frequency of sleep disorders reaches 73%. Chronic sleep disorder can act as a stressor that contributes to the development of pathophysiological changes in the body and increases anxiety. The above determines the need for early detection of sleep disorders and its timely treatment. The current treatment strategy for patients with sleep disorders involves an individualized approach to insomnia therapy. The existing arsenal of drugs used to treat sleep disorders allows this principle to be applied taking into account concomitant diseases (including anxiety disorders). The choice of product is also determined by good tolerability, efficacy and high safety profile. The optimal drug that answers the above requirements is doxylamine succinate, which allows you to effectively stop both anxiety symptoms and sleep disorders. Due to its high safety profile, doxylamine succinate can be used in the treatment of insomnia in pregnant women. The release form determines the convenience of use and the possibility of selecting an individual dose of the drug.

Altered Motor Performance, Sleep EEG, and Parkinson's Disease Pathology Induced by Chronic Sleep Deprivation in Lrrk2G2019S Mice.

Parkinson's disease (PD) is a multifaceted disease in which environmental variables combined with genetic predisposition cause dopaminergic (DAergic) neuron loss in the substantia nigra pars compacta. The mutation of leucine-rich repeat kinase 2 (Lrrk2) is the most common autosomal dominant mutation in PD, and it has also been reported in sporadic cases. A growing body of research suggests that circadian rhythm disruption, particularly sleep-wake abnormality, is common during the early phase of PD. Our present study aimed to evaluate the impact of sleep deprivation (SD) on motor ability, sleep performance, and PD pathologies in Lrrk2G2019S transgenic mice. After two months of SD, Lrrk2G2019S mice at 12 months of age showed an exacerbated PD-like phenotype with motor deficits, a reduced striatal DA level, degenerated DAergic neurons, and altered sleep structure and biological rhythm accompanied by the decreased protein expression level of circadian locomotor output cycles kaput Lrrk2 gene in the brain. All these changes persisted and were even more evident in 18-month-old mice after 6 months of follow-up. Moreover, a significant increase in α-synuclein aggregation was found in SD-treated transgenic mice at 18 months of age. Taken together, our findings indicate that sleep abnormalities, as a risk factor, may contribute to the pathogenesis and progression of PD. Early detection of sleep disorders and improvement of sleep quality may help to delay disease progression and provide long-term clinical benefits.

Association Between Sleep Disorders and Cognitive Impairment in Middle Age and Older Adult Hemodialysis Patients: A Cross-Sectional Study.

Objective: The aims of the current study were to (1) explore the features of overall poor sleep and specific sleep disorders in Chinese middle age and older adult hemodialysis patients; (2) examine the association between sleep disorders and cognitive impairment (CI) in middle age and older patients undergoing hemodialysis in China.Methods: Data of patients undergoing maintenance hemodialysis were collected from the prospective cohort study of CI in Chinese patients undergoing hemodialysis (Registered in Clinical Trials.gov, ID: NCT03251573). We included 613 patients (mean age = 63.7; SD = 7.8) in this study. We assessed sleep conditions using the Pittsburgh Sleep Quality Index (PSQI) questionnaire and cognitive function by the Chinese Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) scale. Then the association between sleep disorders and CI was evaluated using multivariate logistic regression analysis.Results: The prevalence of sleep disorders in this group of 613 hemodialysis patients was 77.0%. Patients with CI were more inclined to have sleep disorders in specific aspects of sleep latency, habitual sleep efficiency, sleep disturbances, and daytime dysfunction (p < 0.05). In multivariate logistic regression analyses, every 1-point increase in global PSQI score was associated with a 1.2-fold increased risk of CI (adjusted OR = 1.201; 95%CI = 1.123–1.284, p < 0.001). For each specific PSQI, every 1-point increase in sleep disturbances score was associated with a 2.6-fold increased risk of CI (adjusted OR = 2.624; 95%CI = 1.891–3.640, p < 0.001), and every 1-point increase in daytime dysfunction score was associated with a 3.7-fold increased risk of CI (adjusted OR = 3.709; 95%CI = 2.653–5.184, p < 0.001), whereas every 1-point increase in sleep duration score was associated with a decreased risk of CI (adjusted OR = 0.600; 95%CI = 0.434–0.830, p = 0.002).Conclusion: Poor sleep quality especially sleep disturbances, daytime dysfunction, and long sleep duration are associated with CI in middle age and older adult hemodialysis patients. Thus, the early detection of sleep disorders may help identify patients with cognitive impairment among hemodialysis individuals.Clinical Trial Registration: [Clinical Trials.gov], identifier [NCT03251573]

The Spectrum of Sleep Disorders Among Children: A Cross-sectional Study at a South Indian Tertiary Care Hospital.

IntroductionSleep problems during infancy and early childhood are fairly common and rarely recognized in pediatric practice. These are mostly related to the initiation and maintenance of night-time sleep. Understanding sleep patterns and disorders associated with sleep is challenging, especially in the pediatric age group. This study was done to estimate the magnitude of sleep disorders in children and to evaluate the associated risk factors.MethodsThis cross-sectional study was carried out among 450 children visiting the pediatric outpatient department of Sri Ramachandra Institute of Higher Education and Research, Chennai, India between November 2018 and June 2019. Children with chronic illnesses and a history of physical or mental trauma in the past six months were excluded. The Sleep Disturbance Scale for Children (SDSC) was used to gather information regarding sleep disorders.ResultsIt was observed that a majority of the participants (72.2%) slept 9-11 hours per day. Among 46.2% of the participants, the time lag between bedtime and sleep time was less than 15 minutes. Overall, sleep problems were present in 34% of the participants. History of sleep problems in infancy, absence of siblings, and parental presence while sleeping emerged as statistically significant risk factors for childhood sleep disorders (p: <0.05).ConclusionWe believe our study provides a basis for exploring the pattern and problems associated with sleep behavior among children. There is a need for setting up routine screening measures in pediatric outpatient departments to facilitate early detection of sleep disorders in order to avoid complications.

Sleep disturbance scale for children as a diagnostic tool for sleep disorders in adolescents

Background. Sleep is an essential needs for every children. Sleep may cause a large effect on mental health, emotional, physical and immune system. If sleeping time is not sufficient, then sleep disturbance may occur. Objective assessment of the sleeping quality can be done by using Polysomnography and Actigraphy, while for assessing the subjectivity of sleeping quality and quantity can be used questionnaires or interviews. Sleep Disturbance Scale for Children or SDSC is a multidimensional sleeping assessment questionnaire.Objective. To determine the quality of sleeping in adolescents using SDSC.Methods. We conducted a diagnostic test study using cross sectional method, during March to April 2015 at the elementary schools in manado. The inclusion criteria were healthy adolescent aged 10 to 12 years, the parents were willing to sign the informed consent and answer the questionnaire, agreed to attach the wristactigraph. All datas were analyzed using chi square test and table 2 x 2 to showed sensitivity, specificity, positive predicitive value and negative predictive value.Results. Of 60 adolescents, 31 were female and 29 were male with the mean age 11.39 years. The results showed the sensitivity of SDSC was 80.6%, the specificity was 37.9%, the positive predictive value was 58.1% and negative predictive value was 64.7%.Conclusions. Sleep Disturbance Scale for Children is a good screening tool for early detection of sleep disorders in a adolescent.

Frequency of Loud Snoring and Metabolic Syndrome among Korean Adults: Results from the Health Examinees (HEXA) Study.

Studies regarding the association between snoring and metabolic abnormalities have been inconsistent. We examine whether snoring frequency and obstructive sleep apnea markers are associated with metabolic syndrome (MetS) among Koreans aged 40–69 years. A total of 72,885 subjects (24,856 men, 48,029 women) from the Health Examinees Gem study between 2009 and 2013 were included. Snoring frequency was grouped into five categories (never, 1–3/month, 1–3/week, 4–5/week, 6+/week). Obstructive sleep apnea markers included breathing interruptions and awakenings. Adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated through logistic regression. Compared with non-snorers, those who snore 6+/week were associated with increased odds for MetS (OR: 2.07, 95% CI: 1.91–2.25, p-trend < 0.0001 among men; OR: 1.45, CI: 1.33–1.58, p-trend < 0.0001 among women). Snoring frequency is associated with MetS and its components in both men and women. Snoring and obstructive sleep apnea markers are important indicators of sleep quality, which may facilitate early detection of sleep disorders and further complications such as MetS.

Quality of sleep in nursing home non-demented residents

admission to nursing homes is often associated with major changes in the way the elderly live, possibly leading to sleep disorders. The aim of our study was to assess sleep quality in the elderly without dementia living in nursing homes, to determine the prevalence of sleep disorders, and to examine the links between sleep quality and sociodemographic features, comorbidities, and degree of dependency. we carried out a cross-sectional survey, involving old people living in Manouba nursing home. Assessment of sleep was performed using Pittsburgh Sleep Quality Index. We excluded subjects with severe motor or sensory impairments as well as individuals with cognitive impairment. thirty-two patients were included in the study. The majority (68.8%) were male, institutionalized for an average period of 6.3 years. Mean age was 71.6 years. More than half of the subjects (56.3%) had depression, and 93.8% had one or more chronic conditions. About one third (37.5%) of residents were dependent in one or more activities of daily living and 25% were confined to bed or to a wheelchair. The majority of residents (73%) were identified as "poor sleepers" based on a global Pittsburgh Sleep Quality Index score >5. Sleep disorders were attributed to nocturia, nocturnal or premature awakenings and pain. We noted a fairly large percentage (18.8%) of hypnotic drugs intake among participants. Our results also showed a strong association between poor sleep quality and depression (p<0.001). early detection of sleep disorders in nursing home residents can help them to get an optimized and efficacious treatment.