Computer recognition of video nystagmus based on digital feature and histogram

Abstract

Objective To establish computer recognition of video nystagmus by analyzing the sample distribution of pilot candidates' video nystagmus with the processing of filtering the deviation and interference caused by blinking and abnormal subjective eye movement. Methods The distribution types of 200 data samples were identified by comparing histogram and Q-Q probability plot. The deviations (caused by blinking and abnormal subjective eye movement) in samples were filtered by analyzing median, range, quartile range and other digital characteristics, and by calculating the upper and lower cut-off point. Results Analysis showed that the spread of nystagmus data was not a normal distribution when certain abnormal eye movements involved until the outliers were filtered. Most of the eye movement deviations (94.9%) could be filtered by processing calculated the upper and lower cut-off point. Conclusions The processing method provides a direct approach to judge the nystagmus data distribution and it can determine the degree of abnormal eye movement interference. Most of the eye movement caused deviations can be filtered by this method. Key words: Electronystagmgraphy; Eye movement measurements; Signal processing, computer-assisted; Histogram; Q-Q probability