S4D-ECG: A shallow state-of-the-art model for cardiac arrhythmia classification (Paper)

This work consists of three main code files. The Model.py file includes the model definition and training process. Evaluation.py evaluates the model's performance on a test set. Finally, Generalization assesses the model's generalization and robustness using different datasets.

Together, these files provide a comprehensive pipeline for developing, training, evaluating, and testing the S4D-ECG model.

The code underwent enhancements based on the work by Hasani et al. in 2022, titled "Liquid Structural State-Space Models."

@article{hasani2022liquid, title={Liquid Structural State-Space Models}, author={Hasani, Ramin and Lechner, Mathias and Wang, Tsun-Huang and Chahine, Makram and Amini, Alexander and Rus, Daniela}, journal={arXiv preprint arXiv:2209.12951}, year={2022} }

Setup

Requirements

This repository requires Python 3.8+ and Pytorch 1.9+.
Other packages are listed in requirements.txt.

pip3 install -r requirement.txt

Citation

@article {Huang2023.06.30.23292069,
	author = {Zhaojing Huang and Luis Fernando Herbozo Contrera and Leping Yu and Nhan Duy Truong and Armin Nikpour and Omid Kavehei},
	title = {S4D-ECG: A shallow state-of-the-art model for cardiac arrhythmia classification},
	elocation-id = {2023.06.30.23292069},
	year = {2023},
	doi = {10.1101/2023.06.30.23292069},
	publisher = {Cold Spring Harbor Laboratory Press},
	URL = {https://www.medrxiv.org/content/early/2023/07/01/2023.06.30.23292069},
	eprint = {https://www.medrxiv.org/content/early/2023/07/01/2023.06.30.23292069.full.pdf},
	journal = {medRxiv}
}