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Acknowledgments

Original Work

This repository contains an independent evaluation of the SeizureTransformer model, originally developed by:

  • Kerui Wu and team (2025)
  • Original paper: "SeizureTransformer: Versatile Seizure Detection Model for Generalization Across Patients, Datasets, and Seizure Types"
  • Original repository: https://github.com/keruiwu/SeizureTransformer

Datasets

Temple University Hospital EEG Seizure Corpus (TUSZ v2.0.3)

  • Temple University Neural Engineering Data Consortium
  • Citation: Shah, V., von Weltin, E., Lopez. S., McHugh, J., Veloso, L., Golmohammadi, M., Obeid, I., and Picone, J. (2018). The Temple University Hospital Seizure Detection Corpus. Frontiers in Neuroinformatics. 12:83. doi: 10.3389/fninf.2018.00083
  • Available at: https://isip.piconepress.com/projects/tuh_eeg/

Evaluation Tools

NEDC EEG Eval v6.0.0

  • Joseph Picone and Iyad Obeid, Temple University
  • Official scoring software from the Neural Engineering Data Consortium
  • Used for computing TAES (Time-Aligned Event Scoring) metrics
  • Available at: https://www.isip.piconepress.com/projects/nedc/

SzCORE Framework

  • Standardized seizure detection evaluation framework
  • Paper: Dan, J. et al. (2025). "SzCORE as a benchmark: report from the seizure detection challenge at the 2025 AI in Epilepsy and Neurological Disorders Conference"
  • Website: https://szcore.org/

Computing Resources

  • GPU processing provided by NVIDIA GeForce RTX 4090
  • Evaluation performed on WSL2 Ubuntu environment

Open Source Dependencies

  • PyTorch - Deep learning framework (Apache 2.0)
  • NumPy - Numerical computing (BSD)
  • scikit-learn - Machine learning utilities (BSD)
  • MNE-Python - EEG processing (BSD)
  • pyEDFlib - EDF file reading (BSD)
  • matplotlib/seaborn - Visualization (PSF/BSD)

Community Contributions

Special thanks to the epilepsy research community for:

  • Establishing standardized evaluation protocols
  • Maintaining public EEG datasets
  • Developing open-source tools for EEG analysis
  • Promoting reproducible research practices

Funding and Support

This independent evaluation was conducted without external funding as a contribution to open science and reproducible research in epilepsy detection.

Disclaimer

This evaluation is independent and not affiliated with the original SeizureTransformer authors or Temple University. All findings and conclusions are our own based on publicly available code and data.

Contact

For questions about this evaluation framework:

For questions about the original SeizureTransformer:

  • Contact the original authors through their publication

For questions about TUSZ dataset or NEDC tools: