通过无接触摄像头进行癫痫发作的跨物种迁移学习预测
Forecasting Epileptic Seizures from Contactless Camera via Cross-Species Transfer Learning
AI总结：
本文提出基于视频的癫痫发作预测任务，利用短预发作视频片段预测后续5秒内是否会发作，通过跨物种迁移学习利用大量啮齿类动物视频数据提升预测准确性，实验结果显示在纯视频设置下预测准确率超过70%。
摘要：
癫痫发作预测是癫痫研究中临床重要但具有挑战性的问题。现有方法主要依赖神经信号如脑电图（EEG），需要专门设备且限制了现实环境中的长期部署。相比之下，视频数据提供了一种非侵入性和可及的替代方案，但现有基于视频的研究主要集中在发作后检测，使发作预测未被探索。本文提出了一种新的视频基癫痫发作预测任务，利用3-10秒的短预发作视频片段预测随后5秒内是否会发作。为了解决标注人类癫痫视频稀缺的问题，我们提出了一种跨物种迁移学习框架，利用大规模啮齿类动物视频数据进行辅助预训练。这使模型能够捕捉与发作相关的行为动态，并在物种间通用。实验结果表明，我们的方法在严格视频-only设置下实现了超过70%的预测准确率，并优于现有基线。这些发现突显了跨物种学习在构建非侵入性、可扩展的癫痫早期预警系统中的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
