Affiliations: [a] University of Tunis ElManar, ISTMT, Laboratory of Biophysics and Medical Technologies, Tunis, Tunisia
| [b] University of Tunis, ENSIT, SIME Laboratory, Montfleury Tunis, Tunisia
| [c] Department of Oto-Rhino-laryngology, La Rabta Hospital, Tunis, Tunisia
| [d] Department of Radiology, La Rabta Hospital, Tunis, Tunisia
Correspondence:
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Corresponding author: Amine Ben Slama, University of Tunis ElManar, ISTMT, Laboratory of Biophysics and Medical Technologies, LR13ES07, Tunis, Tunisia. E-mail: amineslama1@gmail.com.
Abstract: BACKGROUD AND OBJECTIVE:The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition. METHODS:In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects. RESULTS:The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy. CONCLUSIONS:The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.
