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A Non-Linear Support Vector Machine Approach to Testing for Migraine with Aura Using Electroencephalography

机译：一种非线性支持向量机方法，用于使用脑电图对偏头痛进行偏头痛的测试

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In this paper a new migraine analysis method is proposed using EEG (electroencephalography) signals to characterize migraine patients with aura (MwA). The objective of this work is to implement a technique for characterizing and extracting significant, robust and informative features from EEG signals which are representative of the interictal migraine brain state. We extract three brain characteristics using brain network analysis of alpha phase synchronization; transient abnormality analysis using wavelet scale; and finally joint time-frequency analysis using AR modeling. Feature selection and reduction techniques were performed on the sub-features of these three mutually independent features, to combat the over-fit problem as well as to maximize the generality of the classifier. Interpretation of the reduced features resembled to previous migraine studies. Furthermore, extracted features were used as inputs to a 10-fold cross validated non-linear support vector machine (SVM) classifier. The results showed a 92.9% classification accuracy for MwA in the interictal stage from the normal control (NC) group. Findings suggest electrical features for the predisposition of migraine which can lead to possible preventative interventions in the future.
机译：本文采用EEG（脑电图）信号提出了一种新的偏头痛分析方法，以表征Aura（MWA）的偏头痛患者。这项工作的目的是实施一种技术，用于表征和提取来自代表迁移偏头痛脑状态的EEG信号的显着，鲁棒和信息特征。我们利用α相同步的脑网络分析提取三个脑特征;使用小波尺度的瞬态异常分析;最后使用AR造型进行关节时频分析。在这三个相互独立的特征的子特征上执行特征选择和减少技术，用于打击超拟合问题以及最大化分类器的一般性。解释类似于以前的偏头痛研究的减少功能。此外，提取的特征用作10倍交叉验证的非线性支持向量机（SVM）分类器的输入。结果表明，来自正常对照（NC）组的嵌段阶段的MWA分类精度为92.9％。研究结果表明了偏头痛易感性的电气特征，这可能导致未来可能的预防性干预措施。

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