BACKGROUD AND OBJECTIVE:The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition. METHODS:In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects. RESULTS:The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy. CONCLUSIONS:The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.

中文翻译：

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使用眼球震颤参数辨别的混合聚类系统进行前庭障碍诊断。

背景与目的：前庭系统临床表现的控制依赖于最佳诊断。本研究旨在开发和测试一种新的前庭障碍识别自动诊断方案。方法：在这项研究中，我们使用视频 Nysta Gmographic (VNG) 序列对椭圆拟合技术进行分层，以获得分割的瞳孔区域。此外，所提出的方法使我们能够选择最优化的 VNG 特征来有效地对眼球震颤信号进行定量评估。使用多层神经网络分类器 (MNN) 的拟议方案使用涉及 98 名受 VD 影响的患者和 41 名正常受试者的数据集进行测试。结果：新的 MNN 方案仅使用从最初的 13 个参数中选出的 5 个时间和频率参数。该方案生成的结果达到了94%的分类准确率。结论：与其他方法或分类器相比，所开发的专家系统有望解决 VNG 分析问题，实现前庭障碍识别或诊断的准确结果。