Abstract
Electrocardiogram (ECG/EKG) analysis is a vital diagnostic tool for assessing heart conditions, extensively used in clinical applications such as patient monitoring, surgical support, and heart disease research. With the rising demand for automated EKG interpretation, particularly for disease diagnosis and waveform labeling, machine learning models have become essential. However, the scarcity of large, well-labeled EKG datasets poses a significant challenge for training EKG classification models in the supervised form. This has shifted the attention towards unsupervised model pre-training, which often outperforms pure supervised methods when only a limited number of labeled data is available. This study explores the adaptation of the contrastive representation learning framework for EKG classification. Traditional contrastive learning methods rely on data augmentations to create diverse views of the same sample, but these augmentations are domain-specific, difficult to design, and can unpredictably impact model performance across different tasks. In this work, we address these limitations by proposing a novel, augmentation-free approach that integrates seamlessly with existing contrastive frameworks by eliminating their dependence on augmentations and hence their potential drawbacks. We evaluate our approach on the PTB-XL [1] dataset, and demonstrate its benefits in the unsupervised model pre-training step. Our solution offers a promising pathway for enhancing cardiac disease diagnostics in data-constrained environments.