• Corpus ID: 239024647

A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification

@article{Ma2021ARM,
  title={A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification},
  author={Linhai Ma and Liang Liang},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.09759}
}
  • Linhai Ma, Liang Liang
  • Published 19 October 2021
  • Computer Science, Engineering
  • ArXiv
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the human heart. By using deep neural networks (DNNs), interpretation of ECG signals can be fully automated for the identification of potential abnormalities in a patient's heart in a fraction of a second. Studies have shown that given a sufficiently large amount of training data, DNN accuracy for ECG classification could reach human-expert cardiologist level. However, despite of the excellent… 

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