Journal of Sleep Sciences
Volume:3 Issue: 3, Summer-Autumn 2018

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.

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.

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.

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.

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. 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. 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. 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.

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. 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). 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 < 0.0001). 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.

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. The research sample consisted of 160 students of Zanjan University of Medical Sciences, Zanjan, Iran. Then, participants who scored > 15 in Insomnia Severity Index (ISI) and < 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). 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. 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.

Continuous positive airway pressure (CPAP) is a standard therapy for patients with moderate to severe obstructive sleep apnea (OSA). Increased demands for polysomnography (PSG) and CPAP titration have led to long waiting lists and high cost. CPAP prediction formulas derived from sleep and anthropometric parameters are used to set the initial CPAP level during CPAP titration. In the current study, we aimed to compare the pressure derived from prediction formulas with the pressure resulted from CPAP titration in a sample of Iranian patients. In this cross-sectional study, 90 subjects with confirmed OSA in a full PSG who underwent CPAP titration in Baharloo Sleep Clinic, Tehran, Iran, during 2017, were enrolled. All of the participants had Respiratory Disturbance Index (RDI) ≥ 15 in their PSG test. Then, the optimal pressure obtained from manual CPAP titration was compared with the one calculated by different prediction formulas for each patient. The mean CPAP pressure from manual titration was greater than the pressures calculated by four prediction formulas. The difference between mean CPAP pressure obtained by manual titration and pressures calculated by Hoffstein, Lin, and Hukins formulas was statistically significant, whereas mean CPAP pressure obtained by manual titration was not statistically different from Loredo formula (11.7 ± 2.6 vs. 11.0 ± 2.3, P = 0.110). Estimation of optimal therapeutic pressure for CPAP device using several prediction formulas is very similar to pressure found during manual titration study. These formulas can be used in our setting for estimation of optimal CPAP pressure to save time and cost.

Generalized anxiety disorder (GAD) has high comorbidity with other psychological disorders and causes functional impairments. A unified protocol (UP) for transdiagnostic treatment is an emotion-based treatment and can be effective in improving comorbid symptoms. This study aimed at evaluating the effectiveness of UP for transdiagnostic treatment and pharmacotherapy treatment-as-usual (UP + TAU) compared with pharmacotherapy treatment-as-usual (TAU) in reducing the symptoms of GAD and comorbid sleep problems. The present study was a clinical trial and the statistical population of this study consisted of all the people with GAD referred to mental health clinics in Kermanshah, Iran. The participants were 24 individuals with GADs and their comorbid symptoms (sleep problems) and were randomly assigned to control (TAU) and experimental groups (UP + TAU). The participants completed the Penn State Worry Questionnaire (PSWQ), Pittsburgh Sleep Quality Index ‎ (PSQI), and Work and Social Adjustment Scale (WSAS) in the three stages of assessment. One-way analysis of covariance (ANCOVA) was used to analyze the data. Among the participants in the TAU + UP group, the symptoms of GAD and sleep problems (only in follow-up) significantly decreased and overall performance improved in comparison to the TAU group (P > 0.001). However, this significant effect was shown only in some of the components of sleep quality. Based on the findings of this study, UP was effective in reducing the symptoms of GAD and its comorbid symptoms (sleep problems), and improving overall performance. However, the reduction in sleep problems (only in a few components) was statistically significant only at the follow-up stage.

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). 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. 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. 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.

College students suffer from sleep problems. The implications of sleep problems can affect different areas of students’ life, and this, in turn, can be influenced by other psychological characteristics such as anxiety. The aim of this study was to investigate the relationship between state and trait anxiety with components of students' sleep quality. In this descriptive cross-sectional study, 365 students of Kermanshah University of Medical Sciences, Kermanshah, Iran, in 2018, were selected randomly and evaluated by two questionnaires: Pittsburgh Sleep Quality Index (PSQI) and Spielberger's State-Trait Anxiety Inventory (STAI) with ethical considerations. There was a significant difference in sleep quality between different age groups (P < 0.001). As the age of study participants increased, the quality of sleep became poorer. The prevalence of poor sleep in students was about 63%. The highest level of correlation of state and trait anxiety was observed with the sleep latency, and the lowest level of correlation of trait anxiety was observed with the use of sleep medications. Sleep quality is more influenced by trait anxiety and the use of sleep medications is more influenced by state anxiety. It can also be said that the anxiety of the state and trait together predict more subjective sleep quality and contribute less to the prediction of sleep duration.