This work was conducted at the Department of Computer Science, University of Miami. If you find our codes useful, we kindly ask you to cite our work:
Ma, Linhai, and Liang Liang. "Improve robustness of DNN for ECG signal classification: a noise-to-signal ratio perspective." ICLR 2020 workshop AI for Affordable Health. https://arxiv.org/abs/2005.09134
L. Ma and L. Liang, "Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length," 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020, pp. 839-846, doi: 10.1109/ICMLA51294.2020.00137. https://ieeexplore.ieee.org/abstract/document/9356227
Ma, Linhai, and Liang Liang. "A regularization method to improve adversarial robustness of neural networks for ECG signal classification." Computers in Biology and Medicine (2022): 105345. https://www.sciencedirect.com/science/article/pii/S0010482522001378
Python version==3.8.3; Pytorch version==1.5.0; Operation system: CentOS 7. Kernel version: is 3.10.0-1062.1.2.el7.x86_64.
Download data from https://www.kaggle.com/shayanfazeli/heartbeat. Put the csv files at /ecg/ and run preprocess.py.
Prepare dataset:
Download data from http://2018.icbeb.org/Challenge.html. Put the *.mat and *.csv files at "data/CPSC2018/train/" and run preprocess.py
Training and evaluation:
Run "CPSC2018_CNN_NSR.py" for the result of "NSR" in the paper. Run "CPSC2018_CNN_jacob.py" for the result of "jacob" in the paper. Run "CPSC2018_CNN_CE.py" for the result of "CE" in the paper. Run "CPSC2018_CNN_ce_adv_pgd_ls.py" for the result of "advls_$\epsilon$" in the paper.
The parameters can be set in the corresponding .py files and the detailed explaination can be found in the paper.
If you have any question, please contact the authors (l.ma@miami.edu or liang.liang@miami.edu).