A deep convolutional neural network for automated vestibular disorder classification using VNG analysis
A deep convolutional neural network for automated vestibular disorder classification using VNG analysis
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DOI：
10.1080/21681163.2019.1699165
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发表时间：
2019-12-07
影响因子：
1.6
通讯作者：
Trabelsi, Hedi
中科院分区：
文献类型：
--
作者：
Ben Slama, Amine;Mouelhi, Aymen;Trabelsi, Hedi
眩晕是前庭周围性病变的常见症状。眼震障碍的识别可以是一个有用的迹象，以区分不同的前庭疾病。通过在耳鼻喉科临床实践中完成的视频眼震描记（VNG）装置的使用，旋转眼动反应的评估为治疗方案提供了客观、一致和精确的测量。事实上，前庭功能障碍在其特征上引入了重要的变化，其中包括常见VNG检查方法的不同并发症。本文介绍了一种新的方案，以达到分类的眼动信号的视动和热量VNG测试。所提出的方法提供了定量评估和复发性疾病的统一特征。通过使用深度卷积神经网络（CNN）技术，将眼球运动的旋转角度分为两类前庭疾病。所提出的方法进行了验证三个不同的类别：一个事实纳入梅尼埃，神经突和健康受试者。所采用的VNG数据集包含突尼斯的Charles Nicolle和La Rabta医院收治的患者。与以前的工作相比，结果表明，所提出的基于CNN的方法是有效的，在增强头晕分析。
Dizziness is a frequent syndrome of peripheral vestibular lesions. Identification of nystagmus disorder can be a useful sign to discriminate between diverse vestibular diseases. Through the use of videonystagmography (VNG) device accomplished in the clinical practice of ENT department, the assessment of the rotation eye movement response supplies objective, consistent and precise measurements in the therapeutic scheme. In fact, vestibular dysfunctions introduce an important variety in their features which includes different complications for common VNG examination method. This work introduces a new scheme to reach the classification of eye movement signals from optokinetic and caloric VNG tests. The proposed method offers quantitative assessment and uniform characteristics of recurrent-included disease. The rotation angle of eye movements is classified into two classes of vestibular diseases by the use of deep convolutional Neural Network (CNN) technique. The proposed approach is validated on three different categories: a factual incorporated meniere, neurite and healthy subjects. The employed VNG dataset contain patients admitted into both Charles Nicolle and La Rabta hospitals of Tunis. Compared to previous works, the results demonstrate that the proposed CNN-based method is efficient in enhancing dizziness analysis.