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This case study describes a 14-year-old male suffering from significant academic and personal difficulties, who has been diagnosed with depression, schizotypal personality disorder, and learning disabilities. Because of excessive sleepiness, assessment for a potential sleep disorder was performed. An overnight polysomnographic study revealed no primary sleep disorders. Wrist actigraphy revealed a non-24-hour sleep-wake pattern. Delay in temperature rhythm and dissociation with melatonin rhythms were also noted. Treatment with oral melatonin restored normal sleep-wake schedule. In a follow-up psychiatric evaluation, none of the above diagnoses were present. Greater awareness of sleep disorders may prevent psychiatric misdiagnosis of treatable sleep-wake schedule disorders. [ABSTRACT FROM AUTHOR]

Abstract: Arousals are considered one of the main causes of daytime sleepiness. They impede the proper flow of sleep cycles and cause weariness. Manual scoring of arousals is time-consuming, requires expert knowledge, and has high inter-scorer variability. A major difficulty in detecting arousals automatically is the existing variance across patients. Based on data mining techniques, we present a different approach to the automatic detection of arousals that overcomes the hurdle of differences in signal characteristics across patients. Offline we used a training-set of adult patients to define a set of general rules to detect arousals (termed meta-rules). This was done by analyzing the correlations between occurrences of arousals and the EEG, EMG, pulse and SaO2 signals as follows: (1) each signal was mathematically projected into several spaces (termed projected-signals); (2) from each such projected-signal, the algorithm extracted time points that indicated meaningful changes (termed critical-points); (3) data mining techniques were applied to all the critical-points to discover patterns of repeating behavior; (4) classes of patterns which were highly correlated with manually scored arousals were formalized as meta-rules. Online we used a test-set of adult patients from two other different sleep laboratories. Using the meta-rules, the algorithm extracted individual rules for each patient (termed actual-rules), and used them to automatically detect the patients’ arousals. These arousals were significantly correlated (R =0.88, p <0.0001; sensitivity=75.2%, positive predictive value=76.5%) with those detected manually by experts. Since the total number of arousals is a measure of sleep quality, this algorithm constitutes a novel approach to automatically estimate sleep quality. [Copyright &y& Elsevier]

Background: Studies on the prevalence of sleep disorders have found great variability due to different data collection methods and case definitions. We aimed at assessing the prevalence of sleep disorders in a large, unselected population using validated clinical patient records. To the best of our knowledge, this is the first large clinically based study on sleep disorders.Methods: This retrospective study used the computerized data of 2.3 million members of Maccabi Healthcare Services (MHS) public mandated health provider. Among enrolled MHS members alive in June 2018, electronic medical records were searched from January 2010 for sleep disorders using diagnosis codes, sleep medications, and recorded sleep studies.Results: A total of 195,201 patients (9% of the total MHS population) were identified. Patients were 48.3% men and the average age at diagnosis was 50.4 years (SD = 20.9). Prevalence increased with age; 3.2% in children under 10 years, 5.2% in young adults, and 22.3% among seniors aged 75 or older. The two most prevalent disorders were insomnia (7.4%), and sleep-related breathing disorders (2%). Less prevalent disorders included central disorders of hypersomnolence (100 per 100,000), circadian rhythm sleep-wake disorders (49 per 100,000), parasomnias (140 per 100,000), and sleep-related movement disorders (20 per 100,000).Conclusions: The overall prevalence of sleep disorders including insomnia and sleep related breathing disorders in Israel were similar to other western countries despite stressful life events of ongoing war and terrorism. The large sample size allowed us to calculate the prevalence of more rare sleep disorders, which have been generally less known. [ABSTRACT FROM AUTHOR]