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.
@article{arxiv.2211.02642,
title = {A Meta-GNN approach to personalized seizure detection and classification},
author = {Abdellah Rahmani and Arun Venkitaraman and Pascal Frossard},
journal= {arXiv preprint arXiv:2211.02642},
year = {2023}
}