Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs

Bahareh Salafian, Eyal Fishel Ben, Nir Shlezinger, Sandrine de Ribaupierre, Nariman Farsad

Research output: Contribution to conferencePaperpeer-review

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.

Original languageEnglish
Pages424-429
Number of pages6
DOIs
StatePublished - 1 Nov 2021
Event43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society - Guadalajara, Mexico
Duration: 1 Nov 20215 Nov 2021

Conference

Conference43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society
Country/TerritoryMexico
CityGuadalajara
Period1/11/215/11/21

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