GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction. (arXiv:2208.01220v2 [stat.ML] UPDATED)

There has been an increased interest in applying deep neural networks to
automatically interpret and analyze the 12-lead electrocardiogram (ECG). The
current paradigms with machine learning methods are often limited by the amount
of labeled data. This phenomenon is particularly problematic for
clinically-relevant data, where labeling at scale can be time-consuming and
costly in terms of the specialized expertise and human effort required.
Moreover, deep learning classifiers may be vulnerable to adversarial examples
and perturbations, which could have catastrophic consequences, …

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