Machine learning based approach for vestibular disorder diagnostic in videonystagmography

Abstract

Nystagmus is a common sign of peripheral vestibular disorder (VD). Several problems have been recently noted from Video Nystagmo Graphic (VNG) analysis to get relevant diagnosis of VD diseases. The Vestibulo-Ocular Response (VOR) is characterized by a smooth pursuit eye movement in one direction, called slow phase of ocular nystagmus, which is interrupted by saccades (fast phases) in the other direction. The recording of ocular nystagmus during vestibular tests does not quantify the true response of the vestibulo-ocular reflex (VOR). So, to extract the real VOR, our study is focused on nystagmus analysis with videonystagmography (VNG) technique based on measuring amplitude and frequency vibration of eyeball movement. In this paper, we have proposed a fully automatic system based on nystagmus parameter analysis using a pupil detection algorithm and a machine learning approach for VD recognition. Firstly, an estimation of the pupil movement vectors using Hough Transform (HT) is employed to approximate the location of pupil region. Then, temporal and frequency features are computed from the rotation angle variation of the pupil motion. Finally, pertinent features are selected using a statistical criterion for discrimination and classification of the VD disease. Experimental results are employed using two categories which are normal and pathological cases. By discriminating the reduced features with the Support Vector Machine (SVM) technique, 94% of classification accuracy results are achieved. Compared to existing learning methods, the proposed technique is extremely effective to resolve the problem of VD assessment and provide an accurate diagnosis for medical devices.