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We propose a computationally efficient algorithm for seizure detection.Instead of using a purely data-driven approach, we develop a hybridmodel-based/data-driven method, combining convolutional neural networks withfactor graph inference. On the CHB-MIT dataset, we demonstrate that theproposed 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% absoluteimprovement in performance compared to previous data-driven methods. This isachieved while the computational complexity of the proposed technique is afraction of the complexity of prior work, making it suitable for real-timeseizure detection.

Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs