Forecasting Epileptic Seizures from Contactless Camera via Cross-Species Transfer Learning

• URL: http://arxiv.org/abs/2603.12887v1
• Date: Fri, 13 Mar 2026 10:35:14 GMT
• Title: Forecasting Epileptic Seizures from Contactless Camera via Cross-Species Transfer Learning
• Abstract summary: Epileptic seizure forecasting is a clinically important yet challenging problem in epilepsy research.<n>In this work, we formulate a novel task of video-based epileptic seizure forecasting.<n>We propose a cross-species transfer learning framework that leverages large-scale rodent video data for auxiliary pretraining.
• Score: 7.947132798593059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 subsequent 5 seconds. To address the scarcity of annotated human epilepsy videos, we propose a cross-species transfer learning framework that leverages large-scale rodent video data for auxiliary pretraining. This enables the model to capture seizure-related behavioral dynamics that generalize across species. Experimental results demonstrate that our approach achieves over 70% prediction accuracy under a strictly video-only setting and outperforms existing baselines. These findings highlight the potential of cross-species learning for building non-invasive, scalable early-warning systems for epilepsy.

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