A Meta-GNN approach to personalized seizure detection and classification

• URL: http://arxiv.org/abs/2211.02642v2
• Date: Mon, 20 Mar 2023 16:08:30 GMT
• Title: A Meta-GNN approach to personalized seizure detection and classification
• Authors: Abdellah Rahmani, Arun Venkitaraman, Pascal Frossard
• Abstract summary: We propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We train a Meta-GNN based classifier that learns a global model from a set of training patients. 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.
• Score: 53.906130332172324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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