用摄像头视频提前5秒预测癫痫发作，跨物种学习提升准确率
Forecasting Epileptic Seizures from Contactless Camera via Cross-Species Transfer Learning
- 用老鼠视频预训练，迁移学习捕捉跨物种癫痫行为特征
- 仅靠视频数据实现70%以上预测准确率，优于现有方法
- 适合开发无创、可长期使用的癫痫预警系统
癫痫发作预测是癫痫研究中临床意义重大但极具挑战性的问题。现有方法主要依赖脑电图（EEG）等神经信号，需专用设备，难以在真实场景长期部署。相比之下，视频数据提供了一种无创且易获取的替代方案，但已有研究多集中于发作后的检测，发作前预测仍基本空白。本文提出一种基于视频的癫痫发作预测新任务：利用3-10秒的发作前期视频片段，预测未来5秒内是否发生癫痫发作。为缓解标注人类癫痫视频稀缺问题，我们提出跨物种迁移学习框架，利用大规模大鼠视频数据进行辅助预训练，使模型能够捕捉跨物种通用的癫痫行为动态。实验结果表明，在严格仅使用视频的设定下，该方法预测准确率超过70%，显著优于现有基线。这些发现凸显了跨物种学习在构建无创、可扩展的癫痫早期预警系统中的潜力。
原文摘要 · Abstract (English)
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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