A novel automatic diagnostic approach based on nystagmus feature selection and neural network classification

Abstract

Diagnosis of vertigo disorders presents many complications in the evaluation and treatment. In clinical practice, videonystagmography (VNG) tests are still an excellent bedside examination tool for vestibular disorder diagnosis. The parameters of different tests are used to get significant medical characterization of this disease. In this paper, we propose an approach to develop the assessment of vertigo symptom by the selection of the most pertinent VNG parameters using Fisher Linear Discriminant analysis. Therefore, a multilayer neural network (MNN) classifier is applied for automatic VNG dataset analysis based upon the fundamental measurements of normal and affected patients by vestibular disorder. The experimental results prove that the proposed approach is very interesting and helpful for an accurate diagnostic of this disease.