构建首个用于癫痫发作检测的实验鼠视频数据集，支持无创监测研究。
RodEpil: A Video Dataset of Laboratory Rodents for Seizure Detection and Benchmark Evaluation
- 采集19只鼠的俯视与侧视视频，按片段标注正常或发作状态。
- 基于TimeSformer模型实现97%平均F1分数，有效区分发作与正常行为。
- 适合从事癫痫预临床研究、视频分析与行为识别的学者使用。
我们引入了一个经过精心整理的实验室啮齿类动物视频数据集，用于自动检测痉挛事件。该数据集包含来自19只实验鼠的短时（约10秒）俯视和侧视视频片段，每个片段在片段级别被标注为正常活动或癫痫发作。数据集包含10,101个负样本和2,952个正样本。我们详细描述了数据采集、标注协议与预处理流程，并报告了基于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，严格按个体划分训练/测试集以防止数据泄露（任一受试者仅出现在一个折中）。结果表明，TimeSformer架构可实现97%的平均F1分数，准确区分癫痫发作与正常活动。数据集及基线代码已公开发布，以支持癫痫预临床研究中的非侵入式视频监测的可复现性研究。数据集获取：DOI: 10.5281/zenodo.17601357
原文摘要 · Abstract (English)
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
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