Deep learning models for electrocardiograms are susceptible to adversarial attack.

TLDR: A method to construct smoothed adversarial examples for ECG tracings that are invisible to human expert evaluation is developed and it is shown that a deep learning model for arrhythmia detection from single-lead ECG 6 is vulnerable to this type of 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. The development of an algorithm that can imperceptibly manipulate electrocardiographic data to fool a deep learning model for diagnosing cardiac arrhythmia highlights the potential vulnerability of artificial intelligence-enabled diagnosis to adversarial attacks.