Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification
Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification
复制标题
基于深度卷积神经网络的癫痫脑电图 (EEG) 信号分类
DOI：
10.3389/fneur.2020.00375
复制
发表时间：
2020-05-22
影响因子：
3.4
通讯作者：
Zhang, Yingchun
中科院分区：
文献类型：
--
作者：
Gao, Yunyuan;Gao, Bo;Zhang, Yingchun
脑电图（EEG）信号包含大脑电活动的重要信息，被广泛用于辅助癫痫分析。癫痫诊断的一个具有挑战性的因素，不同癫痫状态的准确分类，是特别感兴趣的，并已广泛研究。提出了一种新的基于深度学习的分类方法——癫痫脑电信号分类（EESC）。该方法首先将癫痫脑电图信号转换为功率谱密度能量图（psdds），然后应用深度卷积神经网络（DCNNs）和迁移学习技术对psdds进行特征自动提取，最后将癫痫状态分为4类（间歇期、预测持续时间至30min、预测持续时间至10min和癫痫发作）。该方法在准确率和效率上都优于现有的癫痫分类方法。例如，在CHB-MIT癫痫脑电图数据的案例研究中，其平均分类准确率达到90%以上。
Electroencephalogram (EEG) signals contain vital information on the electrical activities of the brain and are widely used to aid epilepsy analysis. A challenging element of epilepsy diagnosis, accurate classification of different epileptic states, is of particular interest and has been extensively investigated. A new deep learning-based classification methodology, namely epileptic EEG signal classification (EESC), is proposed in this paper. This methodology first transforms epileptic EEG signals to power spectrum density energy diagrams (PSDEDs), then applies deep convolutional neural networks (DCNNs) and transfer learning to automatically extract features from the PSDED, and finally classifies four categories of epileptic states (interictal, preictal duration to 30 min, preictal duration to 10 min, and seizure). It outperforms the existing epilepsy classification methods in terms of accuracy and efficiency. For instance, it achieves an average classification accuracy of over 90% in a case study with CHB-MIT epileptic EEG data.