Automatic seizure detection from electroencephalogram (EEG) plays a vital role in accelerating epilepsy diagnosis. Previous researches on seizure detection mainly focused on extracting time-domain and frequency-domain features from single electrodes, while paying little attention to the positional correlations between different EEG channels of the same subject. Moreover, data imbalance is common in seizure detection scenarios where the duration of nonseizure periods is much longer than the duration of seizures. To cope with the two challenges, a novel seizure detection method based on graph attention network (GAT) is presented. The approach acts on graph-structured data and takes the raw EEG data as input. The positional relationship between different EEG signals is exploited by GAT. The loss function of the GAT model is redefined using the focal loss to tackle data imbalance problem. Experiments are conducted on the CHB-MIT dataset. The accuracy, sensitivity and specificity of the proposed method are 98.89%, 97.10% and 99.63%, respectively.

中文翻译：

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用于脑电图信号癫痫发作检测的具有焦点损失的图注意网络

脑电图 (EEG) 的自动癫痫检测在加速癫痫诊断中起着至关重要的作用。以往对癫痫发作检测的研究主要集中在从单个电极中提取时域和频域特征，而很少关注同一受试者不同脑电通道之间的位置相关性。此外，在非癫痫发作期的持续时间远长于癫痫发作持续时间的癫痫发作检测场景中，数据不平衡很常见。为了应对这两个挑战，提出了一种基于图注意力网络（GAT）的新型癫痫发作检测方法。该方法作用于图形结构数据，并将原始 EEG 数据作为输入。GAT利用了不同EEG信号之间的位置关系。GAT 模型的损失函数使用焦点损失重新定义，以解决数据不平衡问题。实验在 CHB-MIT 数据集上进行。所提出方法的准确度、灵敏度和特异性为 98.89%, 97.10%和 99.63%， 分别。