The increasing technological advancements towards miniaturized physiological measuring devices have enabled continuous monitoring of epileptic patients outside of specialized environments. The large amounts of data that can be recorded with such devices holds significant potential for developing automated seizure detection frameworks. In this work, we present SeizeIT2, the first open dataset of wearable data recorded in patients with focal epilepsy. The dataset comprises more than 11,000 hours of multimodal data, including behind-the-ear electroencephalography, electrocardiography, electromyography and movement (accelerometer and gyroscope) data. The dataset contains 886 focal seizures recorded from 125 patients across five different European Epileptic Monitoring Centers. We present a suggestive training/validation split to propel the development of AI methodologies for seizure detection, as well as two benchmark approaches and evaluation metrics. The dataset can be accessed on OpenNeuro and is stored in Brain Imaging Data Structure (BIDS) format.
Cite
@article{arxiv.2502.01224,
title = {SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy},
author = {Miguel Bhagubai and Christos Chatzichristos and Lauren Swinnen and Jaiver Macea and Jingwei Zhang and Lieven Lagae and Katrien Jansen and Andreas Schulze-Bonhage and Francisco Sales and Benno Mahler and Yvonne Weber and Wim Van Paesschen and Maarten De Vos},
journal= {arXiv preprint arXiv:2502.01224},
year = {2025}
}
Comments
9 pages, 5 figures, 2 tables, paper in submission on Scientific Data Journal from Nature