Learning ECG Image Representations via Dual Physiological-Aware Alignments
arXiv:2604.01526v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are among the most widely used diagnostic tools for cardiovascular diseases, and a large amount of ECG data worldwide appears only in image form. However, most existing automated ECG analysis methods rely on access to raw signal recordings, limiting their applicability in real-world and resource-constrained settings. In this paper, we present ECG-Scan, a self-supervised framework for learning clinically generalized representations from ECG images through dual physiological-aware alignments: 1) Our approach optimizes image representation learning using multimodal contrastive alignment between image and gold-standard signal-text modalities. 2) We further integrate domain knowledge via soft-lead constraints, regularizing the reconstruction process and improving signal lead inter-consistency. Extensive benchmarking across multiple datasets and downstream tasks demonstrates that our image-based model achieves superio
arXiv:2604.01526v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are among the most widely used diagnostic tools for cardiovascular diseases, and a large amount of ECG data worldwide appears only in image form. However, most existing automated ECG analysis methods rely on access to raw signal recordings, limiting their applicability in real-world and resource-constrained settings. In this paper, we present ECG-Scan, a self-supervised framework for learning clinically generalized representations from ECG images through dual physiological-aware alignments: 1) Our approach optimizes image representation learning using multimodal contrastive alignment between image and gold-standard signal-text modalities. 2) We further integrate domain knowledge via soft-lead constraints, regularizing the reconstruction process and improving signal lead inter-consistency. Extensive benchmarking across multiple datasets and downstream tasks demonstrates that our image-based model achieves superior performance compared to existing image baselines and notably narrows the gap between ECG image and signal analysis. These results highlight the potential of self-supervised image modeling to unlock large-scale legacy ECG data and broaden access to automated cardiovascular diagnostics.
Executive Summary
The article introduces ECG-Scan, a self-supervised framework designed to learn clinically robust representations from ECG images, addressing a critical gap in automated cardiovascular diagnostics. Unlike traditional methods that rely on raw signal data, ECG-Scan leverages multimodal contrastive alignment between ECG images and gold-standard signal-text modalities, augmented by soft-lead constraints to enforce physiological consistency. Benchmarking across multiple datasets demonstrates that ECG-Scan outperforms existing image-based baselines and significantly reduces the performance gap between ECG image and signal analysis. The work underscores the potential of self-supervised learning to unlock large-scale legacy ECG data, thereby democratizing access to automated diagnostic tools in resource-constrained settings.
Key Points
- ▸ Introduction of ECG-Scan, a self-supervised framework for learning clinically generalized representations from ECG images, overcoming the dependency on raw signal data.
- ▸ Utilization of dual physiological-aware alignments: multimodal contrastive alignment between ECG images and signal-text modalities, and soft-lead constraints to ensure signal lead inter-consistency.
- ▸ Demonstrates superior performance over existing image-based baselines and narrows the performance gap between ECG image and signal analysis through extensive benchmarking.
Merits
Innovative Multimodal Alignment
The dual physiological-aware alignment strategy effectively bridges the gap between image-based and signal-based ECG analysis, leveraging multimodal contrastive learning and domain-specific soft-lead constraints to enhance clinical relevance.
Resource Efficiency
By enabling automated analysis of ECG images without requiring raw signal data, ECG-Scan addresses a critical limitation in resource-constrained settings, thereby broadening access to cardiovascular diagnostics.
Self-Supervised Learning
The adoption of a self-supervised learning framework reduces the dependency on labeled data, making it scalable and adaptable to diverse clinical scenarios where annotated datasets may be scarce.
Performance Benchmarking
Extensive evaluation across multiple datasets and downstream tasks demonstrates that ECG-Scan not only surpasses existing image-based baselines but also achieves parity with signal-based methods, validating its clinical utility.
Demerits
Limited Generalizability to Non-Standard ECG Formats
The framework may face challenges in generalizing to ECG images with non-standard formats, lead placements, or artifacts, which are common in real-world clinical settings but underrepresented in benchmark datasets.
Dependency on High-Quality Signal-Text Modalities
The multimodal contrastive alignment relies on the availability of high-quality signal-text modality pairs for training. In scenarios where such paired data is unavailable or noisy, the performance of ECG-Scan may degrade.
Computational Complexity
The dual alignment process and soft-lead constraints introduce additional computational overhead, which may pose challenges for deployment in low-resource or edge computing environments.
Expert Commentary
The introduction of ECG-Scan represents a significant advancement in the field of AI-driven cardiovascular diagnostics, particularly in addressing the long-standing challenge of leveraging legacy ECG image data. The dual physiological-aware alignment strategy is a novel contribution that not only enhances the clinical relevance of learned representations but also ensures robustness across different ECG leads and modalities. The demonstrated performance improvements over existing image-based baselines are commendable and suggest that self-supervised learning can indeed bridge the gap between image and signal analysis. However, the framework’s reliance on high-quality signal-text modality pairs and its potential limitations in handling non-standard ECG formats warrant further investigation. Additionally, the lack of explicit interpretability mechanisms may pose a barrier to clinical adoption, despite the model’s strong empirical performance. Future work should focus on enhancing the framework’s generalizability, reducing computational overhead, and incorporating explainability features to ensure seamless integration into clinical workflows. Overall, ECG-Scan is a promising step forward in democratizing access to automated cardiovascular diagnostics, but its real-world applicability will depend on rigorous validation in diverse healthcare settings.
Recommendations
- ✓ Expand the framework to incorporate explicit interpretability mechanisms, such as attention maps or saliency analyses, to enhance clinician trust and facilitate adoption in high-stakes medical decision-making.
- ✓ Investigate the generalizability of ECG-Scan to non-standard ECG formats and noisy data by curating diverse benchmark datasets that include real-world artifacts and variations in lead placement.
- ✓ Develop lightweight variants of ECG-Scan to reduce computational complexity, enabling deployment in low-resource or edge computing environments without compromising performance.
- ✓ Collaborate with regulatory bodies to establish clear pathways for the approval of AI models trained on image-based medical data, ensuring alignment with existing standards for signal-based diagnostic tools.
- ✓ Promote the creation of open-access datasets with paired ECG images and signals to accelerate research and validation of similar multimodal AI frameworks in healthcare.
Sources
Original: arXiv - cs.LG
