Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture

• URL: http://arxiv.org/abs/2410.08559v2
• Date: Wed, 30 Oct 2024 20:33:40 GMT
• Title: Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture
• Authors: Sehun Kim,
• Abstract summary: ECG-JEPA employs a masking strategy to learn semantic representations of ECG data. CroPA enables the model to effectively capture inter-patch relationships. ECG-JEPA is highly scalable, allowing efficient training on large datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a self-supervised learning method for 12-lead Electrocardiogram (ECG) analysis, named ECG Joint Embedding Predictive Architecture (ECG-JEPA). ECG-JEPA employs a masking strategy to learn semantic representations of ECG data. Unlike existing methods, ECG-JEPA predicts at the hidden representation level rather than reconstructing raw data. This approach offers several advantages in the ECG domain: (1) it avoids producing unnecessary details, such as noise, which is common in standard ECG; and (2) it addresses the limitations of na\"ive L2 loss between raw signals. Another key contribution is the introduction of a special masked attention tailored for 12-lead ECG data, Cross-Pattern Attention (CroPA). CroPA enables the model to effectively capture inter-patch relationships. Additionally, ECG-JEPA is highly scalable, allowing efficient training on large datasets. Our code is openly available https://github.com/sehunfromdaegu/ECG_JEPA.

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