RodEpil: A Video Dataset of Laboratory Rodents for Seizure Detection and Benchmark Evaluation
This dataset supports reproducible research in preclinical epilepsy by enabling non-invasive, video-based monitoring, but it is incremental as it focuses on data curation and baseline evaluation.
The authors introduced a curated video dataset of laboratory rodents for automatic seizure detection, achieving an average F1-score of 97% using a transformer-based video classifier.
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