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.
|Number of pages||6|
|State||Published - 1 Nov 2021|
|Event||43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society - Guadalajara, Mexico|
Duration: 1 Nov 2021 → 5 Nov 2021
|Conference||43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society|
|Period||1/11/21 → 5/11/21|