Efficient ECG Representation Learning via Intervention-Based Attribution Alignment

Area of Science:

• Biomedical Engineering
• Artificial Intelligence
• Cardiology

Background:

• Wearable electrocardiogram (ECG) devices generate vast amounts of data, but clinical analysis is hindered by high data annotation costs.
• Conventional deep learning methods for ECG analysis heavily rely on labeled data, exacerbating the annotation bottleneck.

Purpose of the Study:

• To develop a self-supervised learning method for ECG analysis that mitigates the challenges of label scarcity in wearable device data.
• To improve the analytical performance of ECG models using unlabeled data through an intervention-based attribution alignment approach.

Main Methods:

• Proposed a self-supervised learning framework for ECG data integrating an intervention-inspired operation within contrastive learning.
• Extracted compact representations from unlabeled ECG data and employed feature intervention for view-invariant mechanism learning.
• Reduced dependency on labeled data for downstream ECG analysis tasks.

Main Results:

• Achieved performance comparable to state-of-the-art methods on four public ECG datasets for tasks like arrhythmia classification.
• Demonstrated effectiveness using only a fraction of the annotated samples required by traditional methods.
• Validated the method's ability to handle multi-scenario monitoring characteristics of wearable devices.

Conclusions:

• The proposed self-supervised learning method offers a practical solution to reduce ECG data annotation burdens.
• Enables efficient analysis of long-term ECG signals from wearable devices.
• Advances the application of deep learning in cardiology by overcoming data labeling limitations.