We introduce a curated video dataset of laboratory rodents for automatic detection of convulsive events. The dataset contains short (10~s) top-down and side-view video clips of individual rodents, labeled at clip level as normal activity or seizure. It includes 10,101 negative samples and 2,952 positive samples collected from 19 subjects. We describe the data curation, annotation protocol and preprocessing pipeline, and report baseline experiments using a transformer-based video classifier (TimeSformer). Experiments employ five-fold cross-validation with strict subject-wise partitioning to prevent data leakage (no subject appears in more than one fold). Results show that the TimeSformer architecture enables discrimination between seizure and normal activity with an average F1-score of 97%. The dataset and baseline code are publicly released to support reproducible research on non-invasive, video-based monitoring in preclinical epilepsy research. RodEpil Dataset access - DOI: 10.5281/zenodo.17601357

翻译：我们介绍了一个经过精心整理的实验室啮齿动物视频数据集，用于自动检测惊厥事件。该数据集包含单个啮齿动物的短时（10秒）俯视和侧视视频片段，在片段级别标注为正常活动或癫痫发作。数据集包含从19个实验对象采集的10,101个阴性样本和2,952个阳性样本。我们描述了数据整理流程、标注协议和预处理流水线，并报告了使用基于Transformer的视频分类器（TimeSformer）的基线实验结果。实验采用五折交叉验证，并实施严格的按实验对象划分策略以防止数据泄露（同一实验对象仅出现在单一折次中）。结果表明，TimeSformer架构能够区分癫痫发作与正常活动，平均F1分数达到97%。本数据集及基线代码已公开发布，以支持临床前癫痫研究中非侵入式视频监测的可重复性研究。RodEpil数据集访问地址 - DOI: 10.5281/zenodo.17601357