Domain Knowledge is Power: Leveraging Physiological Priors for Self-Supervised Representation Learning in...

Area of Science:

• Cardiology
• Artificial Intelligence
• Machine Learning

Background:

• Electrocardiograms (ECGs) are vital for diagnosing heart conditions.
• Artificial intelligence (AI) ECG analysis is limited by scarce labeled data.
• Self-supervised learning (SSL) can utilize unlabeled data to overcome data limitations.

Purpose of the Study:

• Introduce PhysioCLR, a physiology-aware contrastive learning framework for ECG analysis.
• Enhance generalizability and clinical relevance of AI-based ECG arrhythmia classification.
• Improve ECG diagnostics using label-efficient methods.

Main Methods:

• PhysioCLR uses contrastive learning with domain-specific priors on unlabeled ECG data.
• Integrates ECG physiological similarity cues into the learning process.
• Employs ECG-specific data augmentations and a hybrid loss function.

Main Results:

• PhysioCLR significantly improves mean AUROC by 12% across multiple datasets compared to baselines.
• Demonstrates robust cross-dataset generalization capabilities.
• Learned representations are clinically meaningful and transferable.

Conclusions:

• Physiology-informed SSL, like PhysioCLR, enables learning of clinically relevant ECG features.
• PhysioCLR offers a promising approach for more effective and label-efficient ECG diagnostics.
• This method highlights the potential of integrating domain knowledge into AI for healthcare.