Cao Xincheng, Yao Bin, Chen Binqiang, Sun Weifang, Tan Guowei
School of Aerospace Engineering, Xiamen University, Xiamen, China.
Shenzhen Research Institute of Xiamen University, Shenzhen, China.
Front Neurosci. 2021 Oct 15;15:760987. doi: 10.3389/fnins.2021.760987. eCollection 2021.
Accurate identification of the type of seizure is very important for the treatment plan and drug prescription of epileptic patients. Artificial intelligence has shown considerable potential in the fields of automated EEG analysis and seizure classification. However, the highly personalized representation of epileptic seizures in EEG has led to many research results that are not satisfactory in clinical applications. In order to improve the clinical adaptability of the algorithm, this paper proposes an adversarial learning-driven domain-invariant deep feature representation method, which enables the hybrid deep networks (HDN) to reliably identify seizure types. In the train phase, we first use the labeled multi-lead EEG short samples to train squeeze-and-excitation networks (SENet) to extract short-term features, and then use the compressed samples to train the long short-term memory networks (LSTM) to extract long-time features and construct a classifier. In the inference phase, we first adjust the feature mapping of LSTM through the adversarial learning between LSTM and clustering subnet so that the EEG of the target patient and the EEG in the database obey the same distribution in the deep feature space. Finally, we use the adjusted classifier to identify the type of seizure. Experiments were carried out based on the TUH EEG Seizure Corpus and CHB-MIT seizure database. The experimental results show that the proposed domain adaptive deep feature representation improves the classification accuracy of the hybrid deep model in the target set by 5%. It is of great significance for the clinical application of EEG automatic analysis equipment.
准确识别癫痫发作类型对于癫痫患者的治疗方案和药物处方非常重要。人工智能在自动脑电图分析和癫痫发作分类领域已展现出相当大的潜力。然而，脑电图中癫痫发作的高度个性化表现导致许多研究成果在临床应用中并不理想。为了提高算法的临床适应性，本文提出一种对抗学习驱动的域不变深度特征表示方法，该方法能使混合深度网络（HDN）可靠地识别癫痫发作类型。在训练阶段，我们首先使用带标签的多导联脑电图短样本训练挤压与激励网络（SENet）以提取短期特征，然后使用压缩后的样本训练长短期记忆网络（LSTM）以提取长期特征并构建分类器。在推理阶段，我们首先通过LSTM与聚类子网之间的对抗学习来调整LSTM的特征映射，以使目标患者的脑电图与数据库中的脑电图在深度特征空间中服从相同分布。最后，我们使用调整后的分类器来识别癫痫发作类型。基于TUH脑电图癫痫语料库和CHB - MIT癫痫数据库进行了实验。实验结果表明，所提出的域自适应深度特征表示将混合深度模型在目标集中的分类准确率提高了5%。这对脑电图自动分析设备的临床应用具有重要意义。
