A causal attention network with time frequency channel feature fusion for epileptic seizure prediction

Epilepsy poses ongoing physical and mental threats and causes substantial economic burdens. Better seizure forecasting enables faster medical responses, improving patients' quality of life and lowering healthcare costs. Research mainly focuses on early forecasting within a short preictal window, often too brief for effective drug administration. A major challenge is that a longer preictal phase may resemble the interictal state, making differentiation difficult.

We propose a causal attention network (CANet) with a longer interictal and preictal of 1 h and 2 h respectively as the research object. In the feature extraction, a dilated causal convolution network is employed to extract local features. Causal attention is innovatively incorporated into epilepsy prediction to capture global correlation features. The complementary integration of these two methods enhances feature extraction and enables a more precise distinction between interictal and preictal periods. A double-layer dynamic window algorithm is developed for seizure prediction.

We evaluate the performance on Freiburg and CHB-MIT datasets. On the Freiburg dataset, the sensitivity(Sen) of the 1/2-hour preictal intervals was 100.00%/96.67%, with a false alarm rate per hour (FAR) of 0.0077/h/0.0472/h, and the average prediction time (APT) was 97.59 min. On the CHB-MIT dataset, we achieved Sen of 97.06%/92.31%, FAR of 0.0251/h/0.0666/h, and APT of 94.85 min, under the same conditions.

Our approach outperforms most of the previous methods, and the intracranial EEG (Freiburg) can more effectively distinguish interictal and preictal periods than scalp EEG (CHB-MIT).