Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture
Summary: arXiv:2410.08559v5 Announce Type: replace-cross
Abstract: Electrocardiogram (ECG) captures the heart’s electrical signals, offering valuable information for diagnosing cardiac conditions. However, the scarcity of labeled data makes it challenging to fully leverage supervised learning in the medical domain. Self-supervised learning (SSL) offers a promising solution, enabling models to learn from unlabeled data and uncover meaningful patterns. In this paper, we show that masked modeling in the latent space can be a powerful alternative to existing self-supervised methods in the ECG domain.
We introduce ECG-JEPA, an SSL model for 12-lead ECG analysis that learns semantic representations of ECG data by predicting in the hidden latent space, bypassing the need to reconstruct raw signals. This approach offers several advantages in the ECG domain:
- It avoids producing unnecessary details, such as noise, which is common in ECG;
- It addresses the limitations of naive L2 loss between raw signals.
Another key contribution is the introduction of Cross-Pattern Attention (CroPA), a specialized masked attention mechanism tailored for 12-lead ECG data. ECG-JEPA is trained on the union of several open ECG datasets, totaling approximately 180,000 samples, and achieves state-of-the-art performance in various downstream tasks including:
- Diagnostic classification
- Feature extraction
- Segmentation
Our code is openly available at https://github.com/sehunfromdaegu/ECG_JEPA.
In recent years, the integration of machine learning in healthcare has garnered significant attention, particularly in the analysis of medical data such as ECG. Traditional supervised learning methods often rely heavily on labeled datasets, which are not only time-consuming to obtain but also limited in quantity. This constraint poses a significant challenge in the medical domain, where diverse patient populations and rare conditions complicate the labeling process.
Self-supervised learning (SSL) emerges as a compelling alternative, allowing for the training of models without the need for extensive labeled datasets. By leveraging large amounts of unlabeled data, SSL can uncover hidden patterns and structures within the data, ultimately enhancing the model’s ability to generalize to new, unseen cases.
ECG-JEPA utilizes masked modeling within the latent space to learn robust representations of ECG signals. This innovative approach not only streamlines the learning process but also mitigates common pitfalls associated with raw signal reconstruction, such as noise interference. The implementation of CroPA further enhances the model’s ability to focus on relevant patterns, making it particularly well-suited for the nuanced nature of ECG data.
As healthcare continues to embrace digital transformation, the contributions of models like ECG-JEPA are crucial in advancing diagnostic accuracy and patient care. The accessibility of the code supports collaborative research and encourages further exploration in the field of ECG analysis, paving the way for enhanced cardiology practices in the future.