Video‐based detection of tonic–clonic seizures using a three‐dimensional convolutional neural network
Our model demonstrates high performance in the detection of epileptic seizures from video data using a fine-tuned I3D model and outperforms similar models identified in the literature. This study provides a foundation for future work in real-time EMU seizure monitoring and possibly for reliable, cost-effective at-home detection of tonic-clonic seizures.
Authors
- Mohammad Tabaeizadeh
- Anthony K. Allam (ORCID: https://orcid.org/0000-0003-4409-905X)
- R. Cotton (ORCID: https://orcid.org/0000-0001-5714-1400)
- Hsiang J. Yeh
- John M. Stern (ORCID: https://orcid.org/0000-0002-3549-1642)
- Zulfi Haneef (ORCID: https://orcid.org/0000-0002-0720-1417)
- A Boyne (ORCID: https://orcid.org/0009-0000-0786-4440)
- Brandon M. Brown
Institutions
- Northwestern University (US)
- Michael E. DeBakey VA Medical Center (US)
- Shirley Ryan AbilityLab (US)
- University of California, Los Angeles (US)
- Baylor College of Medicine (US)
Publication Details
- Journal
- Epilepsia
- Published
- 2025-03-24
- DOI
- https://doi.org/10.1111/epi.18381
- Citations
- 2
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 2.86