Background and Objective: Driver drowsiness is one of the major reasons of severe accidents worldwide. In this study, an electroencephalography (EEG) measurement-based approach has been proposed to detect driver drowsiness.

Materials and Methods: The driving tests were conducted in a driving simulator to collect brain data in the alert and drowsy states. Nineteen healthy men participated in these tests. The EEG signals were recorded from the central, parietal, and occipital regions of the brain. 12 features of EEG signal were extracted; then through neighborhood component analysis (NCA), a feature selection method, 6 features including mean, standard deviation (SD), kurtosis, energy, entropy, and power of alpha band in 11-15 Hz, where alpha spindles occur, were selected. For the drowsiness stages assessment, the Observer Rating of Drowsiness (ORD) was applied. Four classifiers including k-nearest neighbor (KNN), support vector machine (SVM), classification tree, and Naive Bayes were employed to classify data.

Results: The classification trees detected drowsiness in the early stage with 88.55%. The classification results showed that if only single-channel P4 was used for detecting drowsiness, the better performance could be achieved in comparison to using data of all channels (C3, C4, P3, P4, O1, O2) together. The best performances were 93.13% which were obtained by the classification tree based on data of single-channel P4.

Conclusion: This study suggested that the driver drowsiness was detectable based on single-channel P4 in the early stage.

Background and Objective: The coronavirus disease 2019 (COVID-19) affects the physiologic and psychological systems of humans and can lead to different degrees of depression, stress, anxiety, and insomnia. This study aimed to evaluate the effect of self-care education on sleep quality and psychological disorders in patients with COVID-19 following discharge.

Materials and Methods: This study was performed on 50 patients with COVID-19, who were educated via telephone. The average time for each interview and education was 20-40 minutes. The education included effective ways to reduce stress, anxiety, and depression as well as sleep hygiene. Data collection tools included three sections: demographic information, Pittsburgh Sleep Quality Index (PSQI) questionnaire, and Depression, Anxiety, and Stress Scale (DASS). These questionnaires were completed by three nurses once 2-3 days after discharge and again one month later by tele-phone. Data were analyzed using SPSS software.

Results: 69% of patients were men with a mean age of 59 years old. Significant difference was observed in each of the subscales of depression, anxiety, and stress, and their total mean (P < 0.0500), in addition, a significant difference was observed in sleep quality of patients with COVID-19 (P < 0.0500) between 2-3 days after discharge and 1 month later after education.

Conclusion: People with COVID-19 had less sleep quality and higher levels of depression, anxiety, and stress. The self-care education regarding sleep hygiene and ways to deal with stress to improve these factors had a significant impact and led to a significant level.

Background and Objective: Obstructive sleep apnea (OSA) needs early detection and effective treatments to reduce the risk of its harmful consequences. The aim of this study was to assess the knowledge and practice of prosthodontists about OSA and oral appliances (OAs) after a period of training and comparative evaluation between two types of virtual education.

Materials and Methods: This study was a randomized clinical trial with two types of educational interventions (Pow-erPoint and podcast) performed among the members of the Association of Prosthodontists (dentists who are specialist in prosthodontics) in 2020. The participants answered to a questionnaire which assessed their knowledge and practical actions about OSA. Data were analyzed using SPSS software and independent-sample t-test.

Results: Group A (PowerPoint) obtained higher scores in all knowledge sections compared to group B (podcast). Totally, the mean scores of group A in knowledge and practical sections were 77.56 ± 9.09 and 81.75 ± 12.39, respectively. In ad-dition, the mean scores of group B in knowledge and practical sections were 74.72 ± 10.79 and 80.69 ± 14.05, respectively. The difference between the mean scores of the two groups in knowledge and practical sections was not significant.

Conclusion: The virtual educational intervention had positive effects on the knowledge and practice of prosthodontists about OSA and OAs. Although the power Point was more effective than podcasts, there was not significant difference between them.

Background and Objective: Disruption of the sleep cycle normal functioning of body system with a significant effect on various dimensions of human lives such as career-related variables. The objective of this study was to investigate the relationship between sleep quality and career adaptability with occupational burnout and to compare them among em-ployees with low and normal sleep quality.

Materials and Methods: In terms of objective and nature, this study was an applied-descriptive, correlational, and causal-comparative study. The statistical population of the study included a private company in Tehran Province, Iran, where 286 people were selected using simple random sampling as the sample and after completing career adaptability, occupational burnout, and sleep quality scales, the relationship between variables was investigated.

Results: The findings indicated a significant negative relationship between sleep quality and occupational burnout and its dimensions. Moreover, a significant positive relationship was found between career adaptability of people with nor-mal sleep and low sleep (P < 0.0500) and people with normal sleep quality showed lower occupational burnout and higher career adaptability. In comparing female and male groups regarding career adaptability and occupational burn-out, the results showed that a significant difference exists between them in emotional exhaustion; females obtained larg-er mean values compared to men and no significant difference was observed among the components.

Conclusion: Given the findings of this study, it can be concluded that sleep, in addition to decreasing occupational burnout, leads to higher career adaptability among employees.

Background and Objective: Obstructive sleep apnea (OSA) is among the critical sleep disorders, and researchers have been investigating its novel diagnostic methods. Polysomnography signals' complexity, difficult visual interpretation, and the need for an efficient algorithm based on simpler signals have made the study of sleep apnea a compelling issue. In this study, the accuracy of chin electromyogram in the diagnosis of OSA was evaluated.

Materials and Methods: The amplitude variation and power spectral density (PSD) of chin electromyograms of 100 patients during apnea and before-after apnea occurrences (non-apnea) periods were compared after complete processing of the raw signal. Two-dimensional (2D) spectrograms related to the specified periods were extracted and fed into the residual neural network (ResNet). The network performance was reported by model evaluation parameters.

Results: The results showed that OSA event influences the patient's chin muscle and increases the amplitude variances and power spectrum of the chin electromyogram. The ResNet-50 deep model classified the dataset of this sleep disorder with about 97% accuracy, which was higher than previous studies in this field.

Conclusion: Chin electromyogram can be introduced as a practical and useful biosignal for accurate OSA diagnosis with a deep classifier without the need for current specialized equipment and multiple vital signals.

Background and Objective: It has been postulated that patients with cancer experience various degrees of poor sleep quality at different points of disease courses. On the other hand, a high proportion of patients with cancer present symp-toms of anxiety and depression. The purpose of the study was to assess the association of sleep quality with anxiety and depression in patients with urological cancers.

Materials and Methods: The present study was a cross-sectional study performed in the Cancer Registry Center at Tehran University of Medical Sciences, Tehran, Iran, in 2019-2020. For eligible patients, demographic data were collected from their records, and Pittsburgh Sleep Quality Index (PSQI) questionnaire, Hamilton Anxiety Rating Scale (HAM-A), and Hamilton Rating Scale for Depression (HAM-D) were completed for each patient.

Results: The mean + SD age of participants was 64.1 ± 14.5 years, and the most of patients were male (90.1%). In total, 142 patients were enrolled in the study, and 92 patients (64.8%) were categorized as patients with poor sleep quality. The mean global score was 7.85 ± 3.94, and the mean of anxiety and depression was 10.85 ± 6.80 and 15.30 ± 4.90, respectively. The regression analysis showed that for one-unit increase in sleep quality score, the anxiety score signifi-cantly increased by 0.98 unit [95% confidence interval (CI): 0.74-1.22, P < 0.001], and for depression significantly increased by 0.69 unit (95% CI: 0.52-0.87, P < 0.001).

Conclusion: More than half of our patients suffer from poor sleep quality, associated with anxiety and depression symptoms.

Adult obstructive sleep apnea (OSA) is associ-ated with various unfavorable outcomes if left untreated, including excessive daytime sleepiness (EDS), decreased personal satisfaction, elevated cardiovascular morbidity and mortality, and in-creased road traffic accidents (1). Continuous pos-itive airway pressure (CPAP), or an oral appli-ance, can allay obstruction; however, many pa-tients have poor adherence, leaving them with long-term morbidities (2, 3).

Considering the advancements in machine learning, neural networks (NNs), as models of the human brain with the ability of generalization, have gained a great deal of attention in numerous fields of science such as sleep medicine for years (1). NNs have been widely used in sleep apnea detection, sleep stage recognition, audio-based snore detection, insomnia identification, detecting sleep-related movement disorder (SRMD), etc.