Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs

• Published in: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2021; pp. 424 - 429
• Main Authors: Salafian, Bahareh, Fishel Ben, Eyal, Shlezinger, Nir, de Ribaupierre, Sandrine, Farsad, Nariman
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.11.2021
• Subjects: Algorithms; Brain modeling; Computational efficiency; Computational modeling; Electroencephalography; Epilepsy - diagnosis; Humans; Inference algorithms; Neural networks; Neural Networks, Computer; Real-time systems; Seizures - diagnosis
• ISSN: 2694-0604
• DOI: 10.1109/EMBC46164.2021.9629917

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.