Sleep Disorder Subjects Research Articles

LSTM based Electroencephalography Analysis for Sleep Disorder Subjects

LSTM based Electroencephalography Analysis for Sleep Disorder Subjects

Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders.

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in β and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

A meta-analysis: Does vitamin D play a promising role in sleep disorders?

BackgroundSleep disorders, one of the most common problems in the general population, have been related to a series of harmful health consequences. Vitamin D appears to be associated with sleep disorders. However, the difference in vitamin D levels between sleep disorder subjects and people without a sleep disorder is unclear. Simultaneously, the influence of vitamin D replenishment on sleep disorders remains controversial.MethodsPubMed, MEDLINE, Web of Science, and Cochrane Library were searched for literatures published until October 2019. Using a random effects model, a meta‐analysis was conducted to calculate the standard mean difference to evaluate the difference in vitamin D concentrations between sleep disorder subjects and normal people and the efficacy of vitamin D supplementation on sleep disorders.ResultsOur study found that the serum vitamin D levels in the sleep disorder subjects were lower than that in the normal people (SMD = −0.75 ng/ml, 95% CI = −0.93, −0.57 ng/ml). Moreover, the Pittsburgh Sleep Quality Index (PSQI)in the subjects with vitamin D supplementation was lower than that in the controls (SMD = −0.45, 95% CI = −0.76, −0.13).ConclusionsVitamin D could play a promising role in sleep disorders. More data are required to confirm the efficacy of vitamin D supplementation for improving sleep disorders.

Study on the association between sleep disorders versus oral health related variables

Background To study the association between sleep quality and oral health related variables, which still have conflicts in the literature. Material and Methods This was a population-based case-control study between subjects with versus without sleep disorders from the Brazilian Public Health System (SUS), city of Maringá (N=1,643). Subjects answered self-reported questionnaires: a) Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD), b) Sleep Assessment Questionnaire (SAQ) and c) North York Dental Health Survey (NYDHS). Results No significant difference was found for gender, marital status, or income; however, non-Caucasians, people with lower levels of education, and those between 20 to 50 years old had worse scores of sleep disorders in the SAQ. Self-perceived oral health, masticatory capacity to eat foods, and gingival bleeding was significantly worse among subjects with self-reported sleep disorders. Self-reported tooth loss, edentulism and use of removable partial dentures (with clasps) or complete dentures showed no significant difference between groups. Self-reported sleep disorder subjects presented significantly higher prevalence of both self-reported tooth and TMJ pain. Conclusions It can be concluded that individuals with self-reported sleep disorders presented worse self-perceived oral health for most studied variables. Key words:Oral health, case control study, sleep; review, gingivitis, periodontitis, tooth loss.

Deep learning approaches for sleep disorder prediction in an asthma cohort

Objective Sleep is a natural activity of humans that affects physical and mental health; therefore, sleep disturbance may lead to fatigue and lower productivity. This study examined 1 million samples included in the Taiwan National Health Insurance Research Database (NHIRD) in order to predict sleep disorder in an asthma cohort from 2002–2010. Methods The disease histories of the asthma patients were transferred to sequences and matrices for the prediction of sleep disorder by applying machine learning (ML) algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), and deep learning (DL) models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Convolution Neural Network (CNN). Results Among 14,818 new asthma subjects in 2002, there were 4469 sleep disorder subjects from 2002 to 2010. The KNN, SVM, and RF algorithms were demonstrated to be successful sleep disorder prediction models, with accuracies of 0.798, 0.793, and 0.813, respectively (AUC: 0.737, 0.690, and 0.719, respectively). The results of the DL models showed the accuracies of the RNN, LSTM, GRU, and CNN to be 0.744, 0.815, 0.782, and 0.951, respectively (AUC: 0.658, 0.750, 0.732, and 0.934, respectively). Conclusions The results showed that the CNN model had the best performance for sleep disorder prediction in the asthma cohort.

A decision support system for automatic sleep staging from HRV using wavelet packet decomposition and energy features

Heart rate variability (HRV) can reflect the relationship between heart rhythm and sleep structure. In order to achieve long-term effective monitoring of sleep and to solve the generalization problem of sleep staging algorithms, HRV signals were used to identify the wake, non-rapid eye movement (NREM) and rapid eye movement (REM) stages of two databases with 11,597 epoches of 16 subjects (8 healthy subjects and 8 sleep disorder). Features were extracted from HRV using three different methods: Hilbert Huang Transform (HHT), Singular Value Decomposition (SVD), and Wavelet Packet Decomposition (WPD). The extracted features were reconstructed into a new sleep score table, classified using random forest (RF) classifier and the results were interpreted using basic statistical criteria. Among these methods, the WPD extraction feature is the most successful. For the sleep staging model trained in the first database, the accuracy of Wake, NREM and REM after WPD extraction features were 80.9%, 88.2% and 65.8% respectively. The second databases were tested using a trained model, in which the Avg.Acc, Avg.Precision, Avg.F and Kappa statistic for healthy subjects were 72.9%, 0.720, 0.716 and 0.439 ± 0.0346. The standard explanations for sleep disorder subjects were 77.8%, 0.780, 0.843 and 0.656 ± 0.0324. The experimental results show that the energy characteristics of WPD extraction are the best. In addition, there was a significant difference in Kappa values (p < 0.05) between healthy subjects and sleep disorder subjects by variance analysis of standard interpretation, and it can be used as a basis for judging whether a subject has a sleep disorder.

Genetic polymorphisms of human melatonin 1b receptor gene in circadian rhythm sleep disorders and controls

Recent studies suggest that melatonin 1b (Mel 1b) receptor, as well as melatonin 1a (Mel 1a) receptor, is involved in the modulation of circadian rhythms in mammals. Mutational analysis was performed in the entire coding region of the human Mel 1b receptor gene using genomic DNA from sleep disorder subjects. We have identified two missense mutations, G24E and L66F. However, neither is likely to be associated with sleep disorders in our study population. One of the subjects with non-24-h sleep-wake syndrome carries missense mutations in both the Mel 1a and Mel 1b receptor genes-