CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy

• URL: http://arxiv.org/abs/2604.11359v1
• Date: Mon, 13 Apr 2026 11:56:43 GMT
• Title: CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy
• Abstract summary: We propose CoRe-ECG, a unified contrastive and reconstructive pretraining paradigm.<n>CoRe-ECG aligns global representations during reconstruction, enabling instance-level discriminative signals to guide local waveform recovery.<n>Our method achieves state-of-the-art performance across multiple downstream ECG datasets.
• Score: 37.81373064028234
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate interpretation of electrocardiogram (ECG) remains challenging due to the scarcity of labeled data and the high cost of expert annotation. Self-supervised learning (SSL) offers a promising solution by enabling models to learn expressive representations from unlabeled signals. Existing ECG SSL methods typically rely on either contrastive learning or reconstructive learning. However, each approach in isolation provides limited supervisory signals and suffers from additional limitations, including non-physiological distortions introduced by naive augmentations and trivial correlations across multiple leads that models may exploit as shortcuts. In this work, we propose CoRe-ECG, a unified contrastive and reconstructive pretraining paradigm that establishes a synergistic interaction between global semantic modeling and local structural learning. CoRe-ECG aligns global representations during reconstruction, enabling instance-level discriminative signals to guide local waveform recovery. To further enhance pretraining, we introduce Frequency Dynamic Augmentation (FDA) to adaptively perturb ECG signals based on their frequency-domain importance, and Spatio-Temporal Dual Masking (STDM) to break linear dependencies across leads, increasing the difficulty of reconstructive tasks. Our method achieves state-of-the-art performance across multiple downstream ECG datasets. Ablation studies further demonstrate the necessity and complementarity of each component. This approach provides a robust and physiologically meaningful representation learning framework for ECG analysis.

Related papers

• ProCA: Progressive Contrastive Alignment for Robust EEG Visual Decoding [47.35612571473169]
  EEG visual decoding aims to recover visual semantics from non-invasive neural time-series signals.<n>Existing methods rely on fixed visual or textual anchors whose semantic relations may become misaligned with EEG representations that vary across trials, subjects, and learning stages.<n>We propose Progressive Contrastive Alignment (ProCA), a unified and model-agnostic framework for adaptive neural-semantic alignment.
  arXiv  Detail & Related papers  (2026-09-04T12:48:25Z)
• Learning Cardiac Latent Representations in Vectorcardiogram Space [54.12639543496968]
  Existing methods operate almost exclusively in the observable ECG signal space.<n>We propose learning a unified latent representation of cardiac electrical activity directly in the VCG space.<n>We introduce LVCG, the first general self-supervised representation learning framework designed to operate in this physically grounded latent space.
  arXiv  Detail & Related papers  (2026-05-29T12:48:12Z)
• Leveraging Foundational Models and Simple Fusion for Multi-modal Physiological Signal Analysis [0.0]
  We adapt the CBraMod encoder for large-scale self-supervised ECG pretraining.<n>We utilize a pre-trained CBraMod encoder for EEG and pre-train a symmetric ECG encoder.<n>Our approach achieves near state-of-the-art performance, demonstrating that carefully designed physiological encoders, even with straightforward fusion, substantially improve downstream performance.
  arXiv  Detail & Related papers  (2025-12-17T09:49:06Z)
• Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation [52.19347532840774]
  We propose SE-Diff, a novel physiological simulator and experience enhanced diffusion model for ECG generation.<n> SE-Diff integrates a lightweight ordinary differential equation (ODE)-based ECG simulator into the diffusion process via a beat decoder.<n>Extensive experiments on real-world ECG datasets demonstrate that SE-Diff improves both signal fidelity and text-ECG semantic alignment.
  arXiv  Detail & Related papers  (2025-11-13T02:57:10Z)
• Versatile and Risk-Sensitive Cardiac Diagnosis via Graph-Based ECG Signal Representation [29.353421682331327]
  VersAtile and Risk-Sensitive cardiac diagnosis employs a graph-based representation to uniformly model heterogeneous ECG signals.<n>VarS stands out by transforming ECG signals into versatile graph structures that capture critical diagnostic features.<n>VarS offers interpretability by pinpointing the exact waveforms that lead to specific model outputs.
  arXiv  Detail & Related papers  (2025-11-11T08:35:50Z)
• High-Fidelity Synthetic ECG Generation via Mel-Spectrogram Informed Diffusion Training [3.864395218585964]
  Development of machine learning for cardiac care is hampered by privacy restrictions on sharing real patient electrocardiogram (ECG) data.<n>In this work, we address two major shortcomings of current generative ECG methods.<n>We build on a conditional diffusion-based Structured State Space Model (SSSD-ECG) with two principled innovations.
  arXiv  Detail & Related papers  (2025-10-07T01:14:53Z)
• CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals [6.327810380749031]
  The study addresses the challenge of reconstructing unseen ECG signals from PPG signals.<n>Data collection processes often introduce noise, complicating ECG reconstruction from PPG even with advanced machine learning models.<n>We develop a novel subject-independent PPG-to-ECG reconstruction model that integrates contrastive learning, adversarial learning, and attention gating.
  arXiv  Detail & Related papers  (2025-02-24T16:53:47Z)
• Graph Structure Refinement with Energy-based Contrastive Learning [56.957793274727514]
  We introduce an unsupervised method based on a joint of generative training and discriminative training to learn graph structure and representation.<n>We propose an Energy-based Contrastive Learning (ECL) guided Graph Structure Refinement (GSR) framework, denoted as ECL-GSR.<n>ECL-GSR achieves faster training with fewer samples and memories against the leading baseline, highlighting its simplicity and efficiency in downstream tasks.
  arXiv  Detail & Related papers  (2024-12-20T04:05:09Z)
• CognitionCapturer: Decoding Visual Stimuli From Human EEG Signal With Multimodal Information [61.1904164368732]
  We propose CognitionCapturer, a unified framework that fully leverages multimodal data to represent EEG signals.<n>Specifically, CognitionCapturer trains Modality Experts for each modality to extract cross-modal information from the EEG modality.<n>The framework does not require any fine-tuning of the generative models and can be extended to incorporate more modalities.
  arXiv  Detail & Related papers  (2024-12-13T16:27:54Z)
• ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion [1.727597257312416]
  We focus on two main scenarios: (i) reconstructing missing signal segments within an ECG lead and (ii) recovering entire leads from signal in another unique lead.<n>We propose ECGrecover, a neural network model trained on a novel composite objective function to address the reconstruction problem.
  arXiv  Detail & Related papers  (2024-05-31T15:17:12Z)
• NERULA: A Dual-Pathway Self-Supervised Learning Framework for Electrocardiogram Signal Analysis [5.8961928852930034]
  We present NERULA, a self-supervised framework designed for single-lead ECG signals. NERULA's dual-pathway architecture combines ECG reconstruction and non-contrastive learning to extract detailed cardiac features. We show that combining generative and discriminative paths into the training spectrum leads to better results by outperforming state-of-the-art self-supervised learning benchmarks in various tasks.
  arXiv  Detail & Related papers  (2024-05-21T14:01:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.