论文标题
个性化癫痫检测和分类的元元方法
A Meta-GNN approach to personalized seizure detection and classification
论文作者
论文摘要
在本文中，我们提出了一个个性化的癫痫发作检测和分类框架，该框架迅速适应了有限的癫痫发作样品中的特定患者。我们结合了两个新型范式，这些范式最近在各种现实世界中都取得了很大的成功：图形神经网络（GNN）和元学习。我们训练一个基于元网格的分类器，该分类器从一组培训患者中学习一个全球模型，以便最终可以使用非常有限的样本将这种全球模型适应新的不见了患者。我们将方法应用于Tusz-Dataset，这是癫痫症的最大且公开可用的基准数据集之一。我们表明，我们的方法在精度上达到82.7％，而在新看见的患者仅20次迭代后，其精度达到82.7％，而F1得分的表现优于基准。
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.
