We propose a computationally efficient algorithm for seizure detection. Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combining convolutional neural networks with factor graph inference. On the CHB-MIT dataset, we demonstrate that the proposed method can generalize well in a 6 fold leave-4-patientout evaluation. Moreover, it is shown that our algorithm can achieve as much as 5% absolute improvement in performance compared to previous data-driven methods. This is achieved while the computational complexity of the proposed technique is a fraction of the complexity of prior work, making it suitable for real-time seizure detection.
@article{arxiv.2108.02372,
title = {Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs},
author = {Bahareh Salafian and Eyal Fishel Ben and Nir Shlezinger and Sandrine de Ribaupierre and Nariman Farsad},
journal= {arXiv preprint arXiv:2108.02372},
year = {2021}
}