Abstract

Info

Author(s):: LIU Xuebing1; 2; GAO Yongbin1; 2; 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201600, China 2. Smart Medical Research Institute, Shanghai University of Engineering Science, Shanghai 201600, China

Keywords:: Keywords: benign paroxysmal positional vertigo binaryzation thresholding template matching

Abstract:: Abstract: Objective To diagnose benign paroxysmal positional vertigo (BPPV) using visual iris recognition concept and neural network for assisting doctors in diagnosis. Methods The pupil and iris were located from the eye movement video of BPPV patients using computer vision binarization and thresholding techniques to capture the eye movement trajectory. During eye movement, iris template matching method was used to extract the feature information of the pupil and eye movement. The obtained data was input into the designed neural network for BPPV diagnosis. Results Through the real-time detection of computer vision technology and the feature learning of network, the proposed method effectively captured and described the eye movement trajectory, and output the eye movement trajectory, thereby assisting doctors in more accurate and faster decision-making. Conclusion Using the proposed method which is relatively simple, non-invasive and accurate to measure torsional eye movements solves the long-standing problems that hinder vision and eye movement, and it can be widely used in basic and clinical researches or diagnostic testing.