Forecasting Epileptic Seizures from Contactless Camera via Cross-Species Transfer Learning
Epileptic seizure forecasting is a clinically important yet challenging problem in epilepsy research. Existing approaches predominantly rely on neural signals such as electroencephalography (EEG), which require specialized equipment and limit long-term deployment in real-world settings. In contrast, video data provide a non-invasive and accessible alternative, yet existing video-based studies mainly focus on post-onset seizure detection, leaving seizure forecasting largely unexplored. In this work, we formulate a novel task of video-based epileptic seizure forecasting, where short pre-ictal video segments (3-10 seconds) are used to predict whether a seizure will occur within the subsequen...
| Authors | Mingkai Zhai, Wei Wang, Zongsheng Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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