Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs

Authors

Bahareh Salafian, Western University
Eyal Fishel Ben, Ben-Gurion University of the Negev
Nir Shlezinger, Ben-Gurion University of the Negev
Sandrine De Ribaupierre, Western UniversityFollow
Nariman Farsad, Ryerson University

Document Type

Conference Proceeding

Publication Date

1-1-2021

Journal

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

First Page

424

Last Page

429

URL with Digital Object Identifier

10.1109/EMBC46164.2021.9629917

Abstract

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-patient-out 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.