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Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction

arXiv:2603.17248v1 Announce Type: new Abstract: Reconstructing a 12-lead electrocardiogram (ECG) from a reduced lead set is an ill-posed inverse problem due to anatomical variability. Standard deep learning methods often ignore underlying cardiac pathology losing vital morphology in precordial leads. We propose Pathology-Aware Multi-View Contrastive Learning, a framework that regularizes the latent space through a pathological manifold. Our architecture integrates high-fidelity time-domain waveforms with pathology-aware embeddings learned via supervised contrastive alignment. By maximizing mutual information between latent representations and clinical labels, the framework learns to filter anatomical "nuisance" variables. On the PTB-XL dataset, our method achieves approx. 76\% reduction in RMSE compared to state-of-the-art model in patient-independent setting. Cross-dataset evaluation on the PTB Diagnostic Database confirms superior generalization, bridging the gap between hardware po

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Youssef Youssef, Jitin Singla · · 1 min read · 9 views
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arXiv:2603.17248v1 Announce Type: new Abstract: Reconstructing a 12-lead electrocardiogram (ECG) from a reduced lead set is an ill-posed inverse problem due to anatomical variability. Standard deep learning methods often ignore underlying cardiac pathology losing vital morphology in precordial leads. We propose Pathology-Aware Multi-View Contrastive Learning, a framework that regularizes the latent space through a pathological manifold. Our architecture integrates high-fidelity time-domain waveforms with pathology-aware embeddings learned via supervised contrastive alignment. By maximizing mutual information between latent representations and clinical labels, the framework learns to filter anatomical "nuisance" variables. On the PTB-XL dataset, our method achieves approx. 76\% reduction in RMSE compared to state-of-the-art model in patient-independent setting. Cross-dataset evaluation on the PTB Diagnostic Database confirms superior generalization, bridging the gap between hardware portability and diagnostic-grade reconstruction.

Executive Summary

This study presents a novel approach to reconstructing 12-lead electrocardiograms (ECGs) from reduced lead sets, addressing the challenges of anatomical variability and underlying cardiac pathology. The proposed Pathology-Aware Multi-View Contrastive Learning framework integrates high-fidelity time-domain waveforms with pathology-aware embeddings learned via supervised contrastive alignment. This method maximizes mutual information between latent representations and clinical labels, filtering anatomical 'nuisance' variables. The study achieves a significant reduction in Root Mean Square Error (RMSE) and demonstrates superior generalization across datasets. The findings have important implications for portable ECG devices and diagnostic-grade reconstruction, bridging the gap between hardware portability and clinical accuracy.

Key Points

  • ▸ Proposes a novel framework for patient-independent ECG reconstruction
  • ▸ Integrates high-fidelity time-domain waveforms with pathology-aware embeddings
  • ▸ Maximizes mutual information between latent representations and clinical labels

Merits

Strength in Addressing Anatomical Variability

The framework effectively filters anatomical 'nuisance' variables, enabling accurate reconstruction across diverse patient populations.

Improvements in Diagnostic-Grade Reconstruction

The study achieves a substantial reduction in RMSE and demonstrates superior generalization across datasets, bridging the gap between hardware portability and clinical accuracy.

Demerits

Limited Generalizability to Non-ECG Applications

While the framework shows promise for ECG reconstruction, its applicability to other medical imaging modalities or non-medical domains remains uncertain.

Complexity and Interpretability Concerns

The multi-view contrastive learning framework may introduce interpretability challenges and require significant computational resources, hindering its adoption in resource-constrained settings.

Expert Commentary

The Pathology-Aware Multi-View Contrastive Learning framework presents a significant advancement in ECG reconstruction, addressing the challenges of anatomical variability and underlying cardiac pathology. While the study demonstrates impressive results, its limitations, particularly in terms of generalizability and interpretability, warrant further investigation. The implications for portable ECG devices and telemedicine are substantial, with the potential to revolutionize remote health services and diagnosis. However, the adoption of this framework will depend on its scalability and adaptability to diverse clinical settings and patient populations.

Recommendations

  • ✓ Further research is needed to explore the framework's applicability to other medical imaging modalities and non-medical domains
  • ✓ Investigate methods to enhance interpretability and reduce computational complexity

Sources

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