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arXiv · 2108.02372

Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs

Bahareh Salafian·Eyal Fishel Ben·Nir Shlezinger·Sandrine de Ribaupierre·Nariman Farsad
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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-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.

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Bahareh Salafian, Eyal Fishel Ben, Nir Shlezinger, Sandrine de Ribaupierre, Nariman Farsad. 2021-08-05. Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs. https://arxiv.org/abs/2108.02372

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