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

arXiv: https://arxiv.org/abs/2603.17248 · HTML: https://mlsanity.com/paper/2603.17248/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 18 Mar 2026.

## Builds on (most cited first, 3 of 3)

- [Multi-Channel Masked Autoencoder and Comprehensive Evaluations for Reconstructing 12-Lead ECG from Arbitrary Single-Lead ECG](https://mlsanity.com/paper/2407.11481/) (2024)
- [Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG](https://mlsanity.com/paper/2506.20683/) (2025)
- [Modally Reduced Representation Learning of Multi-Lead ECG Signals through Simultaneous Alignment and Reconstruction](https://mlsanity.com/paper/2405.19359/) (2024)

## Abstract (arXiv)

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.