University of Tunis ElManar, ISTMT, Laboratory of Biophysics and Medical Technologies, Tunis, Tunisia.
University of Tunis, ENSIT, SIME Laboratory, Montfleury Tunis, Tunisia.
J Xray Sci Technol. 2020;28(5):923-938. doi: 10.3233/XST-200661.
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.
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.
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.
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.
控制前庭系统的临床表现依赖于最佳诊断。本研究旨在开发和测试一种新的自动诊断方案，用于识别前庭障碍。
在这项研究中，我们使用视频眼震图（VNG）序列分层椭圆拟合技术来获得分段瞳孔区域。此外，所提出的方法使我们能够选择最优化的 VNG 特征，有效地对眼震信号进行定量评估。使用多层神经网络分类器（MNN）的建议方案使用涉及 98 名 VD 患者和 41 名正常受试者的数据集进行了测试。
新的 MNN 方案仅使用初始十三个参数中选择的五个时间和频率参数。该方案生成的结果达到了 94%的分类准确性。
与其他方法或分类器相比，所开发的专家系统在解决 VNG 分析问题和实现前庭障碍识别或诊断的准确结果方面具有很大的潜力。
