School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China.
International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, P. R. China.
Int J Neural Syst. 2023 May;33(6):2350031. doi: 10.1142/S0129065723500314. Epub 2023 May 6.
Automatic seizure detection from electroencephalography (EEG) based on deep learning has been significantly improved. However, existing works have not adequately excavate the spatial-temporal information between EEG channels. Besides, most works mainly focus on patient-specific scenarios while cross-patient seizure detection is more challenging and meaningful. Regarding the above problems, we propose a hybrid attention network (HAN) for automatic seizure detection. Specifically, the graph attention network (GAT) extracts spatial features at the front end, and Transformer gets time features as the back end. HAN leverages the attention mechanism and fully extracts the spatial-temporal correlation of EEG signals. The focal loss function is introduced to HAN to deal with the imbalance of the dataset accompanied by seizure detection based on EEG. Both patient-specific and patient-independent experiments are carried out on the public CHB-MIT database. Experimental results demonstrate the efficacy of HAN in both experimental settings.
基于深度学习的自动脑电（EEG） seizure 检测已经得到了显著的改善。然而，现有的工作并没有充分挖掘 EEG 通道之间的时空信息。此外，大多数工作主要集中在特定于患者的场景，而跨患者 seizure 检测更具挑战性和意义。针对上述问题，我们提出了一种用于自动 seizure 检测的混合注意力网络（HAN）。具体来说，图注意力网络（GAT）在前端提取空间特征，Transformer 在后端获取时间特征。HAN 利用注意力机制充分提取 EEG 信号的时空相关性。在基于 EEG 的 seizure 检测中，我们引入焦点损失函数来处理数据集的不平衡问题。我们在公共 CHB-MIT 数据库上进行了特定于患者和独立于患者的实验。实验结果表明 HAN 在这两种实验设置下都具有很好的效果。
