VNG technique for a convenient vestibular neuritis rating. Issue 5 (2nd September 2020)

• Record Type: Journal Article
• Authors: Sahli, Hanene; Ben Slama, Amine; Bouzaiane, Sami; Marrakchi, Jihene; Boukriba, Seif; Sayadi, Mounir
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 8:Issue 5(2020)
• Page Start: 571
• Page End: 580
• Publication Date: 2020-09-02
• Subjects: Vestibular neuritis (VN) -- dsPIC implementation -- videonystagmographic test (VNG) -- multilayer neural network (MNN) -- classification; Imaging systems in biology -- Periodicals; Imaging systems in medicine -- Periodicals; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; 616.0757
• DOI: 10.1080/21681163.2020.1741034
• Languages: English
• ISSNs: 2168-1163

Abstract

ABSTRACT: Vestibular Neuritis (VN) joins to the most significant public health concern. A lot of videonystagmographic (VNG) datasets are admitted to clinical assessment methods, which impose a serious problem in term of complexity. The aim of this work is to develop a simple, fast and intelligent method to identify subjects with a high risk of VN disease. This paper proposes a real-time digital signal controller (dsPIC) based system with a digital output display indicating the VN existence. The clinical feature inputs are extracted from the current VNG analysis. The proposed method has been experimented on a database including 73 patients affected by vestibular neuritis (VN) proceeded with saccadic, kinetic and caloric tests for basic measures. Moreover, the VNG characteristics are divided into two groups: VN and HL cases. The obtained classification results have achieved the best precision when applying the supervised multilayer neural network (MNN). As stated in the performance assessment, we recorded more than 0.9576 for accuracy of detected vestibular neuritis supplied by ENT pathologists which reveals the highest Positive Predictive Values with the specialist's result (PPV = 0.9528, Negative Likehood ratio <0.2). This framework shows ENT application of vestibular dysfunction as a successful tool for automatic VN evaluation without expert intervention.