Abstract

Vertigo is a common sign related to a problem with the brain or vestibular system. Detection of ocular nystagmus can be a support indicator to distinguish different vestibular disorders. In order to get reliable and accurate real time measurements from nystagmus response, video-oculography (VOG) plays an important role in the daily clinical examination. However, vestibular diseases present a large diversity in their characteristics that leads to many complications for usual analysis. In this paper, we propose a novel automated approach to achieve both selection and classification of nystagmus parameters using four tests and a pupil tracking procedure in order to give reliable evaluation and standardized indicators of frequent vestibular dysfunction that will assist clinicians in their diagnoses. Indeed, traditional tests (head impulse, caloric, kinetic and saccadic tests) are applied to obtain clinical parameters that highlight the type of vertigo (peripheral or central vertigo). Then, a pupil tracking method is used to extract temporal and frequency nystagmus features in caloric and kinetic sequences. Finally, all extracted features from the tests are reduced according to their high characterization degree by linear discriminant analysis, and classified into three vestibular disorders and normal cases using sparse representation. The proposed methodology is tested on a database containing 90 vertiginous subjects affected by vestibular Neuritis, Meniere's disease and Migraines. The presented technique highly reduces labor-intensive workloads of clinicians by producing the discriminant features for each vestibular disease which will significantly speed up the vertigo diagnosis and provides possibility for fully computerized vestibular disorder evaluation.

摘要

眩晕是与大脑或前庭系统问题有关的常见症状。眼震的检测可以作为区分不同前庭疾病的支持指标。为了从眼球震颤反应中获得可靠和准确的实时测量值，视频眼图 (VOG) 在日常临床检查中发挥着重要作用。然而，前庭疾病在其特征上表现出很大的多样性，导致通常分析的许多并发症。在本文中，我们提出了一种新的自动化方法，使用四项测试和瞳孔跟踪程序来实现眼球震颤参数的选择和分类，以便对频繁的前庭功能障碍提供可靠的评估和标准化指标，以帮助临床医生进行诊断。事实上，传统测试（头部冲动、热量、动力学和眼跳测试）用于获得突出眩晕类型（外周或中枢性眩晕）的临床参数。然后，使用瞳孔跟踪方法提取热量和动力学序列中的时间和频率眼震特征。最后，所有从测试中提取的特征通过线性判别分析根据它们的高表征程度进行缩减，并使用稀疏表示将其分为三种前庭障碍和正常情况。提议的方法在包含 90 名受前庭神经炎、美尼尔氏病和偏头痛影响的眩晕对象的数据库上进行测试。