No abstract

Eye movements during closed eyes closely reflect changes of the arousal level during transition from wakefulness to sleep. Because they contain both rapid and slow eye movements (REM and SEM), it has been difficult to detect them automatically. Hiroshige recently developed the method of linear regression analysis for automatic detection of the two types of eye movements, and we have developed a template matching method for autodetection. The aim of the present study was to compare both auto-detection methods and visual scoring for REM and SEM. The results revealed high agreement between the two quantitative methods and the visual scoring, indicating that auto-detection of eye movements is useful for quantitative evaluation of arousal level.

The two nap sleep test (TNST) was developed and its usefulness for detecting sleepiness in longdistance drivers has been reported. This study's authors attempted to apply the TNST as a clinical test of sleepiness. A normal control group (n = 29), an obstructive sleep apnea syndrome (OSAS) group (n = 9), and another sleep disorder group (n = 6) participated. As a result of polysomnography, the sleep latency and sleep time did not differ among the groups. In contrast, the frequency of micro-arousal and movement arousal was significantly higher in the OSAS group than in the other groups. The TNST is thought to be useful for evaluating disturbance of sleep maintenance.