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

This study proposes a causal attention network (CANet) that combines dilated causal convolution and attention mechanisms to predict seizures during extended preictal periods (up to 2 hours before onset). Testing on standard epilepsy datasets showed high sensitivity (92-100%) and low false alarm rates, with average prediction times exceeding 90 minutes, potentially allowing sufficient time for therapeutic intervention.