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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
