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

• URL: http://arxiv.org/abs/2603.17248v1
• Date: Wed, 18 Mar 2026 01:06:30 GMT
• Title: Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction
• Abstract summary: Pathology-Aware Multi-View Contrastive Learning is a framework that regularizes the latent space through a pathological manifold.<n>Our architecture integrates high-fidelity time-domain waveforms with pathology-aware embeddings learned via supervised contrastive alignment.<n>On the PTB-XL dataset, our method achieves approx. 76% reduction in RMSE compared to state-of-the-art model in patient-independent setting.
• Score: 0.12277343096128711
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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