A Meta-GNN approach to personalized seizure detection and classification
This addresses the problem of personalized epilepsy monitoring for patients, but it is incremental as it combines existing paradigms like GNNs and meta-learning.
The paper tackles personalized seizure detection and classification by proposing a Meta-GNN framework that adapts to new patients with limited samples, achieving 82.7% accuracy and 82.08% F1 score on the TUSZ dataset.
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.