# ACL-ECG: Anatomy-Aware Contrastive Learning for Multi-Lead Electrocardiograms

## TL;DR

This paper introduces ACL-ECG, a new self-supervised learning method for ECG analysis that uses anatomical knowledge to improve performance with less labeled data.

## Contribution

The novel contribution is integrating cardiac anatomical relationships into contrastive learning for ECGs.

## Key findings

- ACL-ECG outperforms state-of-the-art methods by up to 1.29% in AUROC and 3.57% in AUPRC.
- ACL-ECG achieves comparable performance to fully supervised training with only 10% of labeled data.
- Ablation studies confirm the effectiveness of anatomy-aware augmentation and contrastive objectives.

## Abstract

Deep learning has achieved impressive progress in automated electrocardiogram (ECG) analysis, yet its performance still relies heavily on large-scale labeled datasets. As ECG annotation requires cardiologists, this process is costly and time-consuming, limiting its scalability in clinical practice. Contrastive learning offers a promising alternative by enabling the extraction of generalizable representations from unlabeled ECG data. In this study, we propose Anatomy-Aware Contrastive Learning for ECG (ACL-ECG), a self-supervised method that incorporates cardiac anatomical relationships into contrastive learning. ACL-ECG employs a physiology-aware augmentation strategy to generate rhythm-preserving augmented views, including random scale cropping, cardiac-cycle masking, and temporal shifting. Furthermore, ECG leads are grouped into four anatomically meaningful regions—anterior, inferior, septal, and lateral—and region-level contrastive objectives are introduced to promote intra-region consistency while enhancing inter-region discriminability. Extensive evaluations of downstream tasks demonstrate that ACL-ECG consistently outperforms state-of-the-art contrastive baselines under linear probing, achieving improvements of up to 1.29% in the area under the receiver operating characteristic curve (AUROC) and 3.57% in the area under the precision–recall curve (AUPRC). Moreover, when fine-tuned using only 10% of labeled data, ACL-ECG attains a performance comparable to fully supervised training, effectively reducing annotation requirements by approximately 5∼8×. Ablation studies further confirm the contributions of both the physiology-aware augmentation strategy and the anatomy-aware contrastive objective. Overall, ACL-ECG enhances representation quality without increasing annotation burden, and provides a promising and anatomy-informed foundation for self-supervised ECG analysis in label-scarce settings.