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

TL;DR

This paper introduces a novel, non-invasive video-based method for forecasting epileptic seizures using cross-species transfer learning, achieving over 70% accuracy and outperforming existing approaches.

Contribution

It pioneers the use of contactless video data for seizure forecasting and leverages rodent data for cross-species transfer learning to improve prediction.

Findings

Achieved over 70% prediction accuracy with video-only data.

Outperformed existing baseline methods.

Demonstrated the effectiveness of cross-species transfer learning.

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.