  The electrocardiogram (ECG) is a ubiquitous diagnostic modality.
Convolutional neural networks (CNNs) applied towards ECG analysis require large
sample sizes, and transfer learning approaches result in suboptimal performance
when pre-training is done on natural images. We leveraged masked image modeling
to create the first vision-based transformer model, HeartBEiT, for
electrocardiogram waveform analysis. We pre-trained this model on 8.5 million
ECGs and then compared performance vs. standard CNN architectures for diagnosis
of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST
elevation myocardial infarction using differing training sample sizes and
independent validation datasets. We show that HeartBEiT has significantly
higher performance at lower sample sizes compared to other models. Finally, we
also show that HeartBEiT improves explainability of diagnosis by highlighting
biologically relevant regions of the EKG vs. standard CNNs. Thus, we present
the first vision-based waveform transformer that can be used to develop
specialized models for ECG analysis especially at low sample sizes.
