Automatic Detection of Single Slow Eye Movements and Analysis of their Changes at Sleep Onset

Abstract

An algorithm that can automatically identify slow eye movements from the electro-oculogram is presented. The automatic procedure is trained using the visual classification of an expert scorer. The algorithm makes use of both the spectral and morphological signal information to detect single slow eye movements. On the basis of this detection some parameters that characterize the slow eye movements (amplitude, duration, velocity and number) are extracted. A few possible applications of the algorithm are shown by means of a preliminary study: the average patterns of slow eye movements parameters at sleep onset are evaluated for healthy volunteers and for patients affected by obstructive sleep apnea syndrome. Finally, general considerations are drawn regarding the clinical interest of the study.