CE-stSENet

Original PyTorch implementation of "Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network" (IEEE Transactions on Neural Systems and Rehabilitation Engineering 2020).

Paper: https://ieeexplore.ieee.org/document/8995501

Requirements

The code was implemented using Python 3.8.3 and the following packages:

• torch==1.4.0
• numpy==1.18.5
• scipy==1.5.0

Datasets

CE-stSENet with maximum mean discrepancy-based information maximizing loss is evaluated on three public datasets:

• Bonn Dataset
• TUSZ Dataset
• CHB-MIT dataset

Main Results

Citations

If you find the paper or this repo useful, please cite:

@ARTICLE{8995501,
  author={Li, Yang and Liu, Yu and Cui, Wei-Gang and Guo, Yu-Zhu and Huang, Hui and Hu, Zhong-Yi},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, 
  title={Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network}, 
  year={2020},
  volume={28},
  number={4},
  pages={782-794},
  doi={10.1109/TNSRE.2020.2973434}}

Contacts

For questions or help, feel welcome to write an email to sy1803113@buaa.edu.cn or liyang@buaa.edu.cn.