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Deep learning models for electrocardiograms are susceptible to adversarial attack

Abstract

Electrocardiogram (ECG) acquisition is increasingly widespread in medical and commercial devices, necessitating the development of automated interpretation strategies. Recently, deep neural networks have been used to automatically analyze ECG tracings and outperform physicians in detecting certain rhythm irregularities1. However, deep learning classifiers are susceptible to adversarial examples, which are created from raw data to fool the classifier such that it assigns the example to the wrong class, but which are undetectable to the human eye2,3. Adversarial examples have also been created for medical-related tasks4,5. However, traditional attack methods to create adversarial examples do not extend directly to ECG signals, as such methods introduce square-wave artefacts that are not physiologically plausible. Here we develop a method to construct smoothed adversarial examples for ECG tracings that are invisible to human expert evaluation and show that a deep learning model for arrhythmia detection from single-lead ECG6 is vulnerable to this type of attack. Moreover, we provide a general technique for collating and perturbing known adversarial examples to create multiple new ones. The susceptibility of deep learning ECG algorithms to adversarial misclassification implies that care should be taken when evaluating these models on ECGs that may have been altered, particularly when incentives for causing misclassification exist.

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Fig. 1: Demonstration of disruptive adversarial examples.
Fig. 2: Accuracy of the network in classifying adversarial examples and clinician success rate in distinguishing authentic ECGs from adversarial examples.
Fig. 3: Perturbing a known adversarial example to generate multiple new ones.

Data availability

The dataset can be accessed from https://physionet.org/challenge/2017/.

Code availability

The code is available from https://github.com/XintianHan/ADV_ECG.

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Acknowledgements

X.H. and R.R. were supported in part by the NIH under award no. R01HL148248. We thank W. Lee, S. Mohan, M. Goldstein, A. Li, A.M. Puli, H. Singh, M. Sudarshan, and W. Whitney.

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X.H. and R.R. designed the problem and performed all the experiments. All authors wrote the manuscript.

Corresponding authors

Correspondence to Xintian Han or Rajesh Ranganath.

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The authors declare no competing interests.

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Peer review information Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 An adversarial example created by Projected Gradient Descent (PGD) method.

This adversarial example contains square waves that are physiologically implausible.

Extended Data Fig. 2 Demonstration of three procedures to show the existence of the adversarial examples.

a, We add a small amount of Gaussian noise to the adversarial example and smooth it to create a new signal. b, For intersected signals, we concatenate the left half of one signal with the right half of the other to create a new one. c, We sample uniformly from the band and smooth to create a new signal.

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Han, X., Hu, Y., Foschini, L. et al. Deep learning models for electrocardiograms are susceptible to adversarial attack. Nat Med 26, 360–363 (2020). https://doi.org/10.1038/s41591-020-0791-x

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