  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.
