A new neural network method for peripheral vestibular disorder recognition using VNG parameter optimisation. (2018)

• Record Type: Journal Article
• Authors: Slama, Amine Ben; Mouelhi, Aymen; Sahli, Hanene; Manoubi, Sondes; Lahiani, Rim; Salah, Mamia Ben; Trabelsi, Hedi; Sayadi, Mounir
• Is Part Of: International journal of biomedical engineering and technology. Volume 27:Number 4(2018)
• Page Start: 321
• Page End: 336
• Publication Date: 2018
• Subjects: vertigo -- videonystagmographic -- vestibular tests -- nystagmus -- FLDA -- Fisher linear discriminant analysis -- MNN -- multilayer neural network; Biomedical engineering -- Periodicals; 610.28
• Languages: English
• ISSNs: 1752-6418

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.