A Survey of Augmentation Techniques for Enhancing ECG Representation Through Self-Supervised Contrastive Learning

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Electrocardiogram (ECG) is a primary tool for assessing heart electrical activity.
• Traditional ECG analysis relies heavily on human interpretation.
• Machine learning (ML) offers automated disease detection but requires large labeled datasets, often unavailable for rare conditions.

Purpose of the Study:

• To investigate the efficacy of self-supervised learning (SSL) in overcoming data scarcity for ECG analysis.
• To implement and evaluate the Momentum Contrast (MoCo) framework for SSL on a clinical ECG dataset.
• To assess the impact of data augmentation strategies on SSL performance for Low Left Ventricular Ejection Fraction (LVEF) detection.

Main Methods:

• Utilized a large-scale clinical ECG dataset for training.
• Implemented the Momentum Contrast (MoCo) framework, a type of SSL.
• Evaluated the model's performance on the downstream task of LVEF detection.
• Compared SSL performance across various input augmentation techniques and dataset sizes.

Main Results:

• SSL demonstrated potential in improving ECG analysis, particularly for tasks like LVEF detection.
• The study identified that optimal data augmentation hyperparameters for SSL varied significantly with training dataset size.
• Performance gains from SSL were dependent on the chosen augmentation strategies and the scale of the dataset.

Conclusions:

• Self-supervised learning (SSL) is a viable approach to address data limitations in clinical ECG analysis.
• Augmentation strategies in SSL for ECG data require careful tuning based on the specific downstream task and available dataset size.
• Further research into adaptive augmentation for SSL in cardiology is warranted.