Title: A new neural network method for peripheral vestibular disorder recognition using VNG parameter optimisation

Authors: Amine Ben Slama; Aymen Mouelhi; Hanene Sahli; Sondes Manoubi; Rim Lahiani; Mamia Ben Salah; Hedi Trabelsi; Mounir Sayadi

Addresses: University of Tunis, ENSIT, SIME, 1008, Montfleury, Tunis, Tunisia; University Tunis ElManar, ISTMT (LRBTM), Tunis, Tunisia ' University of Tunis, ENSIT, SIME, 1008, Montfleury, Tunis, Tunisia ' University of Tunis, ENSIT, SIME, 1008, Montfleury, Tunis, Tunisia ' Department of Oto-Rhino-Laryngology, Head and Neck Surgery, Charles Nicolle Hospital, Tunis, Tunisia ' Department of Oto-Rhino-Laryngology, Head and Neck Surgery, Charles Nicolle Hospital, Tunis, Tunisia ' Department of Oto-Rhino-Laryngology, Head and Neck Surgery, Charles Nicolle Hospital, Tunis, Tunisia ' University Tunis ElManar, ISTMT (LRBTM), Tunis, Tunisia ' University of Tunis, ENSIT, SIME, 1008, Montfleury, Tunis, Tunisia

Abstract: The peripheral vestibular disorder (VD) diagnosis is a required task to ensure the efficiency of the treatment that needs complementary examinations of vertigo. In clinical practice, different tests of videonystagmographic (VNG) technique are used to detect the presence of vestibular dysfunction disease. The topographical diagnosis of this disease presents a large diversity in its characteristics that show a mixture of problems for usual etiological analysis methods. In this paper, we propose an automatic classification method of VD by analysing and reducing the VNG parameters based on a determined criterion. Therefore, a multilayer neural network (MNN) classifier is applied for VNG dataset based on the fundamental measurements of normal and patients affected by VD. The experimental results confirm that the proposed approach is very interesting and helpful for an accurate diagnostic of this disease.

Keywords: vertigo; videonystagmographic; vestibular tests; nystagmus; FLDA; Fisher linear discriminant analysis; MNN; multilayer neural network.

DOI: 10.1504/IJBET.2018.094299

International Journal of Biomedical Engineering and Technology, 2018 Vol.27 No.4, pp.321 - 336

Received: 28 Jan 2017
Accepted: 13 Sep 2017
Published online: 28 Aug 2018 *

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