In this paper, we propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We achieve this by combining two novel paradigms that have recently seen much success in a wide variety of real-world applications: graph neural networks (GNN), and meta-learning. We train a Meta-GNN based classifier that learns a global model from a set of training patients such that this global model can eventually be adapted to a new unseen patient using very limited samples. We apply our approach on the TUSZ-dataset, one of the largest and publicly available benchmark datasets for epilepsy. We show that our method outperforms the baselines by reaching 82.7% on accuracy and 82.08% on F1 score after only 20 iterations on new unseen patients.

翻译：本文提出了一种个性化癫痫检测与分类框架，该框架能从有限的癫痫发作样本中快速适应特定患者。我们通过结合两种近期在多种实际应用场景中取得显著成功的新范式——图神经网络与元学习——实现了这一目标。我们训练了一个基于元图神经网络的分类器，它从一组训练患者中学习全局模型，使得该全局模型最终能利用极少量样本适应新的未知患者。我们在图兹数据集（癫痫领域最大且公开可用的基准数据集之一）上应用了该方法。实验表明，在仅经过20次迭代后，我们的方法在新患者数据上取得了82.7%的准确率和82.08%的F1分数，优于基线方法。