Automatic Nystagmus Detection from Video using Machine Learning

Abstract

Nystagmus, which causes the eyeballs to move or shake spasmodically and independently of the willpower, may occur during everyday vision, or it may be caused by abnormalities in the brain, ears, or other parts of the body. Currently, videoculography (VOG) is used to diagnose nystagmus, but current analysis methods have problems with misrecognition of the pupil due to the narrowness of the eye slit, the pupil being obscured by long eyelashes, and the influence of blinking, which affect stable nystagmus detection. In this study, we developed an automatic nystagmus detection method using a combination of image processing and machine learning based on video images taken by magnifying the eye area, and evaluated its performance.