Improving ECG Classification with Diverse Neural Networks
Author Information
Author(s): Wiedeman Christopher, Wang Ge
Primary Institution: Rensselaer Polytechnic Institute
Hypothesis
Can feature decorrelation and Fourier partitioning improve the robustness of ECG classification against adversarial attacks?
Conclusion
The proposed methods enhance the robustness of ECG classification models against adversarial attacks while maintaining performance on unperturbed data.
Supporting Evidence
- Ensemble methods can estimate uncertainty in predictions.
- Adversarial attacks can mislead traditional models, but diversified features improve robustness.
- Feature decorrelation and Fourier partitioning can be applied to enhance model performance.
Takeaway
This study shows that using different features in neural networks can help them make better decisions when faced with tricky inputs, like those that try to trick them.
Methodology
The study tested ensemble methods with feature decorrelation and Fourier partitioning on ECG classification tasks against adversarial attacks.
Limitations
The study's methods may require extensive hyperparameter tuning and may not generalize to all types of adversarial attacks.
Digital Object Identifier (DOI)
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