RodEpil: A Video Dataset of Laboratory Rodents for Seizure Detection and Benchmark Evaluation

TL;DR

This paper presents a new video dataset of laboratory rodents for seizure detection, along with baseline experiments using a transformer-based classifier, to advance non-invasive epilepsy research.

Contribution

It introduces a curated, labeled video dataset of rodents for seizure detection and provides baseline results with a transformer model, supporting reproducible research.

Findings

Transformer-based classifier achieves 97% F1-score

Dataset includes over 13,000 clips from 19 subjects

Strict subject-wise cross-validation prevents data leakage

Abstract

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