Department of Neurosurgery, School for Mental Health and Neuroscience of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands; Center for Medical Electronics and Computing, M S Ramaiah Institute of Technology, Bengaluru, India.
Center for Medical Electronics and Computing, M S Ramaiah Institute of Technology, Bengaluru, India.
Neural Netw. 2020 Apr;124:202-212. doi: 10.1016/j.neunet.2020.01.017. Epub 2020 Jan 25.
Recognition of epileptic seizure type is essential for the neurosurgeon to understand the cortical connectivity of the brain. Though automated early recognition of seizures from normal electroencephalogram (EEG) was existing, no attempts have been made towards the classification of variants of seizures. Therefore, this study attempts to classify seven variants of seizures with non-seizure EEG through the application of convolutional neural networks (CNN) and transfer learning by making use of the Temple University Hospital EEG corpus. The objective of our study is to perform a multi-class classification of epileptic seizure type, which includes simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic-clonic, and non-seizures. The 19 channels EEG time series was converted into a spectrogram stack before feeding as input to CNN. The following two different modalities were proposed using CNN: (1) Transfer learning using pretrained network, (2) Extract image features using pretrained network and classify using the support vector machine classifier. The following ten pretrained networks were used to identify the optimal network for the proposed study: Alexnet, Vgg16, Vgg19, Squeezenet, Googlenet, Inceptionv3, Densenet201, Resnet18, Resnet50, and Resnet101. The highest classification accuracy of 82.85% (using Googlenet) and 88.30% (using Inceptionv3) was achieved using transfer learning and extract image features approach respectively. Comparison results showed that CNN based approach outperformed conventional feature and clustering based approaches. It can be concluded that the EEG based classification of seizure type using CNN model could be used in pre-surgical evaluation for treating patients with epilepsy.
识别癫痫发作类型对于神经外科医生了解大脑皮层连接至关重要。尽管已经存在从正常脑电图（EEG）中自动识别癫痫发作的方法，但尚未尝试对癫痫发作的变体进行分类。因此，本研究试图通过应用卷积神经网络（CNN）和迁移学习，利用坦普尔大学医院 EEG 语料库对七种非癫痫 EEG 的癫痫发作变体进行分类。我们的研究目的是对癫痫发作类型进行多类分类，包括简单部分性、复杂部分性、局灶性非特异性、全身性非特异性、失神、强直和强直-阵挛性发作以及非发作性。19 通道 EEG 时间序列被转换为频谱图堆栈，然后作为输入提供给 CNN。使用 CNN 提出了以下两种不同的模式：（1）使用预训练网络的迁移学习，（2）使用预训练网络提取图像特征并使用支持向量机分类器进行分类。为了识别最适合本研究的网络，使用了以下十种预训练网络：Alexnet、Vgg16、Vgg19、Squeezenet、Googlenet、Inceptionv3、Densenet201、Resnet18、Resnet50 和 Resnet101。使用迁移学习和提取图像特征的方法分别实现了 82.85%（使用 Googlenet）和 88.30%（使用 Inceptionv3）的最高分类准确率。比较结果表明，基于 CNN 的方法优于传统的特征和聚类方法。可以得出结论，基于 CNN 模型的 EEG 癫痫发作类型分类可用于治疗癫痫患者的术前评估。
