Tehran University of Medical Sciences Journal of Sleep Sciences 2476-2938 6 1-2 2022 04 19 1 10 Sara Houshmand Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran Reza Kazemi Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran Hamed Salmanzadeh Department of Industrial engineering, K. N. Toosi University of Technology, Tehran, Iran 2021 06 30 2021 08 23 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. https://jss.tums.ac.ir/index.php/jss/article/view/205

Tehran University of Medical Sciences Journal of Sleep Sciences 2476-2938 6 1-2 2022 04 19 48 50 Reza Erfanian Otolaryngology Research Center, Tehran University of Medical Sciences, Tehran, Iran Reihaneh Heidari Otolaryngology Research Center, Tehran University of Medical Sciences Tehran Iran Arezu Najafi Occupational Sleep Research Center, Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran. Behrouz Amirzargar Otolaryngology Research Center, Tehran University of Medical Sciences, Tehran Iran 2020 11 24 2021 07 05 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). https://jss.tums.ac.ir/index.php/jss/article/view/182

Tehran University of Medical Sciences Journal of Sleep Sciences 2476-2938 6 1-2 2022 04 19 11 18 EN Gordafarid Moradian Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran Neda Doozandeh Tabarestani Tracheal Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran Shirin Esmaeili Dolabi Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran Fatemeh Monjazabi Department of Medical-Surgical Nursing, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran Mahsa Farahanipour Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran Negin Mojarad Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran 2020 09 22 2022 04 19 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. https://jss.tums.ac.ir/index.php/jss/article/view/178

Tehran University of Medical Sciences Journal of Sleep Sciences 2476-2938 6 1-2 2022 04 19 51 52 Morteza Zangeneh Soroush Bio-intelligence Research Unit, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran 2021 05 31 2021 07 04 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. https://jss.tums.ac.ir/index.php/jss/article/view/202

Tehran University of Medical Sciences Journal of Sleep Sciences 2476-2938 6 1-2 2022 04 19 19 24 Somayeh Niakan School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran Somayeh Allahyari School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran Amirhossein Vakilli School of Dentistry, Tehran University of Medical Sciences, Tehran, Iran 2021 05 14 2021 10 09 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. https://jss.tums.ac.ir/index.php/jss/article/view/201

Tehran University of Medical Sciences Journal of Sleep Sciences 2476-2938 6 1-2 2022 04 19 25 31 Atefe Noorollahi Master of Clinical Psychology, Islamic Azad University, Saveh Branch, Saveh, Iran Nooshin Pordelan Department of Education and Counseling, Islamic Azad University, Science and Research Branch, Tehran, Iran Sadaf Khalijian Department of Educational Administration and Human Resources Development, School of Educational Sciences and Psychology, Ferdowsi University of Mashhad, Mashhad, Iran Amir hasan koohi Department of Education, Zeynabiyeh, Farhangian University, Tehran, Iran Morteza Abbasi Department of Education and Counseling, Shahid Chamran University of Ahvaz, Ahvaz, Iran 2021 06 06 2021 09 02 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. https://jss.tums.ac.ir/index.php/jss/article/view/203

Tehran University of Medical Sciences Journal of Sleep Sciences 2476-2938 6 1-2 2022 04 19 32 40 Behrouz Moradhasel Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Ali Sheikhani Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Oldooz Aloosh Department of Internal Medicine, Iran University of Medical Sciences, Tehran, Iran; Masih Daneshvari Hospital,School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Nader Jafarnia Dabanloo Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran 2021 10 22 2021 12 30 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. https://jss.tums.ac.ir/index.php/jss/article/view/215

Tehran University of Medical Sciences Journal of Sleep Sciences 2476-2938 6 1-2 2022 04 19 41 47 Pantea Arya Occupational Sleep Research Center, Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran Zeinab Ahadi Urology Research Center, Tehran University of Medical Sciences, Tehran, Iran Journal of Sleep Sciences 2476-2938 3 3-4 2019 06 12 90 94 EN Nahid Nikoee Department of Management, School of Humanities, Electronic Branch, Islamic Azad University, Tehran, Iran Mohammad Seyed Hoseini Department of Industrial Engineering, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran Arezu Najafi Occupational Sleep Research Center, Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran. Amin Amali Otorhinolaryngology Research Center, Tehran University of Medical Sciences, Tehran, Iran; Occupational Sleep Research Center, Tehran University of Medical Sciences, Tehran, Iran AND Department of Otorhinolaryngology, Imam Khomeini Educational Complex Hospital, Valiasr Hospital, Tehran University of Medical Sciences, Tehran, Iran Behrouz Amirzargar Otorhinolaryngology Research Center AND Occupational Sleep Research Center AND Department of Otorhinolaryngology, Imam Khomeini Educational Complex Hospital, Valiasr Hospital, Tehran University of Medical Sciences, Tehran, Iran Reihaneh Heidari Otorhinolaryngology Research Center, Tehran University of Medical Sciences, Tehran, Iran; Occupational Sleep Research Center, Tehran University of Medical Sciences, Tehran, Iran AND Department of Otorhinolaryngology, Imam Khomeini Educational Complex Hospital, Valiasr Hospital, Tehran University of Medical Sciences, Tehran, Iran 2018 03 13 2019 02 23 Background and Objective: Polysomnography (PSG) is the gold standard for diagnosis of sleep disorders. Several software