ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training [Cite]

by: Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan

This work has been accepted for publication in the IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI).

Requirmenets:

• Python3.7
• Pytorch=='1.6'
• Numpy
• Sklearn
• Pandas
• openpyxl
• umap

Prepare datasets

We used three public datasets in this study:

• Sleep-EDF (EDF)
• SHHS dataset (S1, S2)

Data of each domain should be split into train/validate/test splits. The domains IDs should be (a, b, c, ...).

For example, the data files of domain 'a' should be train_a.pt, val_a.pt, and test_a.pt, such that train_a.pt is a dictionary.

train_a.pt = {"samples": x-data, "labels: y-labels}, and similarly val_a.pt, and test_a.pt.

Training model

You can update different hyperparameters in the model by updating config_files/config.py file.

To train the model, use this command:

python train_CD.py --experiment_description differentBatchSizes --run_description bs_128 --num_runs 1 --device cuda --plot_umap False

Results

The results include the final classification report of the average performance and a seprate folder for each cross-domain scenario having its log file and its own classification report.

Citation

IF you found this work useful for you, please consider citing it.

@article{emadeldeen_adast,
  author={Eldele, Emadeldeen and Ragab, Mohamed and Chen, Zhenghua and Wu, Min and Kwoh, Chee-Keong and Li, Xiaoli and Guan, Cuntai},
  journal={IEEE Transactions on Emerging Topics in Computational Intelligence}, 
  title={ADAST: Attentive Cross-Domain EEG-Based Sleep Staging Framework With Iterative Self-Training}, 
  year={2023},
  volume={7},
  number={1},
  pages={210-221},
  doi={10.1109/TETCI.2022.3189695
}

Contact

Emadeldeen Eldele
School of Computer Science and Engineering (SCSE)
Nanyang Technological University, Singapore
Email: emad0002{at}e.ntu.edu.sg