Journal of Sleep Sciences <p>The “Journal of Sleep sciences (JSS) “<strong>(<span style="text-decoration: underline;">رتبه علمی- پژوهشی) </span></strong>is the official scientific quarterly publication affiliated with Occupational Sleep Research Center (OSRC) of Tehran University of Medical Sciences. JSS is also official journal of Iranian Sleep Medicine Society. The main goals of journal are to improve the knowledge and awareness of clinicians and research professionals about the latest findings in prevention, diagnosis, treatment and etiology of sleep disorders. We would be very delighted to receive your Original Papers, Review Articles, Short communications, Case reports and Scientific Letters to the Editor on the all areas of sleep sciences.&nbsp;</p> Tehran University of Medical Sciences en-US Journal of Sleep Sciences 2476-2938 Is Speech Frequency Thresholds Associated with STOP-BANG Score of Commercial Drivers? <p><strong>Background and Objective:</strong> Obstructive sleep apnea (OSA) is a common sleep-disordered breathing (SDB) characterized by intermittent hypoxemia (IH). OSA and IH are considered risk factors for increased hearing thresholds as well. Furthermore, thresholds of speech frequency affect personal fitness for driving. Thus, main purpose of this study was to assess the association between speech frequency thresholds and OSA among commercial drivers.</p> <p><strong> Materials and Methods:</strong> This cross-sectional study was conducted on 1000 commercial drivers who were referred to obtain a health license at the Occupational Medicine Clinic of Baharloo Hospital, Tehran, Iran. Blood pressure, neck size, weight, and height were recorded. Validated Persian version of Epworth Sleepiness Scale (ESS) and STOP-BANG questionnaire were completed by the participants. STOP-BANG score ≥ 3 was defined as high risk for OSA. Fast blood sugar (FBS), triglyceride (TG), and total cholesterol (TC) were measured for all drivers. Hearing threshold levels of all subjects were recorded by pure tone audiometry (PTA) in frequencies of 500, 1000, 2000, and 3000 Hz for each ear in decibels (dB).</p> <p><strong> Results:</strong> The mean age of the participants was 43.0 ± 9.9 years. The mean ESS and STOP-BANG scores of the participants were 3.1 ± 2.8 and 1.8 ± 0.8, respectively. 237 (23.7%) drivers were high-risk for OSA. Participants with OSA had significantly higher thresholds of speech frequencies compared to the low-risk ones (P &lt; 0.0001).</p> <p><strong>Conclusion:</strong> OSA may be also considered as a risk factor for increased thresholds of speech frequency among commercial drivers. During drivers’ periodic medical examination, evaluation of OSA as a strong risk factor for increasing hearing thresholds is recommended.</p> Arezu Najafi Nafiseh Naeemabadi Maryam Saraei Khosro Sadeghniiat-Haghighi Masoomeh Mahmoodi-Afsah Ania Rahimi-Golkhandan ##submission.copyrightStatement## 2019-06-12 2019-06-12 3 3-4 63 67 Exploration of Reliable Parameters Scored by Automated Analysis in Polysomnography <p><strong>Background and Objective:</strong> Polysomnography (PSG) is the gold standard for diagnosis of sleep disorders. Several software programs are available to analyze sleep tests according to available guidelines and decrease the time and cost of PSG analysis. This study aimed to compare the parameters of automated analyzer software with analysis of trained technician (manual analysis).</p> <p><strong> Materials and Methods:</strong> Twenty patients who underwent full-night PSG were randomly selected. A sleep technologist who was blind to the study, scored sleep stages and respiratory events according to recommended criteria of American Academy of Sleep Medicine (AASM) 2013, then an auto analysis was done using N-7000 amplifier. Results of auto analysis and manual analysis were compared. Descriptive statistics and paired t-test were used for data analysis.</p> <p><strong> Results:</strong> Total sleep time (TST) and sleep efficiency (SE) calculated by auto analysis was significantly more than manual analysis (511.82 ± 35.34 vs. 396.85 ± 75.97 for TST and 95.47 ± 3.74 vs. 74.14 ± 35.34 for SE, respectively). Furthermore, there was no concordance for sum of apneas and hypopneas during TST. However, calculated number of hypopneas in non-rapid eye movement (NREM) stage in auto analysis and manual analysis was quite similar. The least precision was observed in scoring of stages 3 and REM for auto analysis scoring and the most similarity for scoring of stage N2.</p> <p><strong> Conclusion:</strong> Detecting hypopneas in NREM stage by auto analysis maybe the reliable parameter that could help the technicians during analysis of sleep test. There is a need for more advanced automated algorithms. Furthermore, manual analysis is superior to automated one in PSG analysis according to the current results.</p> Nahid Nikoee Mohammad Seyed Hoseini Arezu Najafi Amin Amali Behrouz Amirzargar Reihaneh Heidari ##submission.copyrightStatement## 2019-06-12 2019-06-12 3 3-4 90 94 The Effectiveness of the Cognitive Behavioral Group Therapy Based on Edinger and Carney’s Protocol on Insomnia and Bedtime Procrastination in Patients with Insomnia <p><strong>Background and Objective:</strong> Due to the high incidence of insomnia in students and its effects on physical and psychological health, the present study was conducted to investigate the effectiveness of cognitive behavioral group therapy (CBGT) on insomnia and bedtime procrastination in students.</p> <p><strong>Materials and Methods:</strong> The research sample consisted of 160 students of Zanjan University of Medical Sciences, Zanjan, Iran. Then, participants who scored &gt; 15 in Insomnia Severity Index (ISI) and &lt; 45 in Symptom Checklist-25 (SCL-25) were selected. ISI and Bedtime Procrastination Scale (BPS) were performed at three time intervals. Eight 90-minute sessions of CBGT were performed on the experimental group based on Edinger and Carney's protocol, and the control group received a booklet on sleep hygiene. The data were analyzed by repeated measures analysis of variance (ANOVA) and one-way analysis of covariance (ANCOVA).</p> <p><strong> Results:</strong> CBGT was significantly related to the reduced insomnia severity and bedtime procrastination. The difference between the two interventions in outcome variables showed that CBGT was significantly more effective than educational intervention in decreasing the rate of insomnia, but the difference between the two interventions was not significant in terms of bedtime procrastination. In other words, CBGT had a significant effect on study outcome and it was more effective than educational intervention.</p> <p><strong>Conclusion:</strong> Based on the Edinger and Carney’s protocol, CBGT was effective in reducing insomnia severity, but it did not directly affect bedtime procrastination. It seems that the inclusion of modalities for addressing bedtime procrastination increases the effectiveness of this treatment specifically for insomnia in students.</p> Zolfaghar Hosnavi Omid Saed Saeede Zenozian ##submission.copyrightStatement## 2019-06-12 2019-06-12 3 3-4 68 74 Estimation of Driver Drowsiness Using Blood Perfusion Analysis of Facial Thermal Images in a Driving Simulator <p><strong>Background and Objective:</strong> Driver drowsiness is a major cause of fatal driving accidents worldwide, which can be prevented by an early detection. Driver drowsiness results in reduction of blood flow and change in facial thermal patterns. In this paper, a drowsiness detection system was designed to estimate the level of drowsiness by analyzing facial skin temperature patterns.</p> <p><strong>Materials and Methods:</strong> 12 subjects drove on a highway in a driving simulator, while their facial temperature image was captured by a thermal camera. The subjects’ drowsiness levels were independently estimated by the observer rating of drowsiness (ORD) method. Facial blood vessels were located by a four-step algorithm and tracked in each frame. The mean value of the pixel intensities of the facial blood regions formed a sequence of data. Variations of facial skin temperature signals were investigated for three main blood vessels.</p> <p><strong> Results:</strong> Facial skin temperature decreased at three levels of sleepiness from wakefulness to extreme drowsiness. Temperature near the supratrochlear, angular, and facial arteries decreased by 0.54 ºC, 0.33 ºC, and 0.32 ºC from wakefulness to extreme drowsiness, respectively. All subjects except one experienced skin temperature decrease from wakefulness to extreme drowsiness.</p> <p><strong> Conclusion:</strong> It was shown that the drivers’ facial blood vessels temperature decreased as the level of drowsiness increased. Driver drowsiness can be detected by monitoring the change in the facial skin temperature signal during driving. Facial thermal imaging is a promising non-intrusive method to detect driver drowsiness.</p> Masoumeh Tashakori Ali Nahvi Azadeh Shahiidian Serajeddin Ebrahimian Hadi Kiashari Hamidreza Bakhoda ##submission.copyrightStatement## 2019-06-12 2019-06-12 3 3-4 45 52 Investigation of Sleep Deprivation Effect on Driver’s Electromyography Signal Features in a Driving Simulator <p><strong>Background and Objective:</strong> Sleep deprivation is an important cause of driver drowsiness. Surface electromyography (sEMG) of the upper arm and the shoulder is an important physiological signal affected by the driver drowsiness. The objective of this paper is to derive the pattern of sleep-deprived drivers’ sEMG.</p> <p><strong>Materials and Methods:</strong> The tests were conducted on 7 men with no sleep disorder aged between 25 and 50 years in a driving simulator. Each subject participated in the tests once without sleep deprivation and another time with two hours of sleep in the 24-hour period before the tests. The sEMG signal from the upper arm and shoulder muscles were measured for the mid deltoid, clavicular portion of the pectoralis major, and triceps and biceps long heads. Four features including power spectral kurtosis (SK), mean frequency, absolute amplitude, and root mean square (RMS) were extracted.</p> <p><strong>Results:</strong> The k-nearest neighbors (k-NN) algorithm classifier detected drowsiness with 90% accuracy, 82% precision, 77% sensitivity, and 94% specificity. Driver’s sleep deprivation can be detected through sEMG signal with 85% accuracy, 80% precision, 70% sensitivity, and 88% specificity.</p> <p><strong>Conclusion:</strong> The sEMG signal amplitude and the frequency content of the sleep-deprived subjects were higher than those of the normal subjects by 37% and 15%, respectively. For the sleep-deprived subjects, muscle contraction did not change much in transition between the last two levels of drowsiness, while the normal subjects experienced 27% drop in this transition. At the last level of drowsiness, the sleep-deprived subjects experienced mental drowsiness without significant change in the muscle contraction level.</p> Mohammad Mahmoodi Ali Nahvi ##submission.copyrightStatement## 2019-06-12 2019-06-12 3 3-4 53 62