The automatic detection of epilepsy is essentially the classification of EEG signals of seizures and nonseizures, and its purpose is to distinguish the different characteristics of seizure brain electrical signals and normal brain electrical signals. In order to improve the effect of automatic detection, this study proposes a new classification method based on unsupervised multiview clustering results. In addition, considering the high-dimensional characteristics of the original data samples, a deep convolutional neural network (DCNN) is introduced to extract the sample features to obtain deep features. The deep feature reduces the sample dimension and increases the sample separability. The main steps of our proposed novel EEG detection method contain the following three steps: first, a multiview FCM clustering algorithm is introduced, and the training samples are used to train the center and weight of each view. Then, the class center and weight of each view obtained by training are used to calculate the view-weighted membership value of the new prediction sample. Finally, the classification label of the new prediction sample is obtained. Experimental results show that the proposed method can effectively detect seizures.

中文翻译：

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利用多视图聚类算法和深度特征的癫痫检测方法。

癫痫的自动检测本质上是癫痫发作和非癫痫发作的EEG信号的分类，其目的是区分癫痫发作的脑电信号和正常的脑电信号的不同特征。为了提高自动检测的效果，本研究提出了一种基于无监督的多视图聚类结果的新分类方法。另外，考虑到原始数据样本的高维特征，引入了深度卷积神经网络（DCNN）来提取样本特征以获得深度特征。深度功能可减小样品尺寸并增加样品可分离性。我们提出的新颖的EEG检测方法的主要步骤包括以下三个步骤：首先，介绍了多视图FCM聚类算法，训练样本用于训练每个视图的中心和权重。然后，将通过训练获得的每个视图的类中心和权重用于计算新预测样本的视图加权成员值。最后，获得新的预测样本的分类标签。实验结果表明，该方法可以有效地检测癫痫发作。