Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis
arXiv:2607.01145
The paper introduces ER-JEPA, a lightweight self‑supervised learning framework that builds hierarchical representations for multivariate time‑series data, demonstrated on 12‑lead ECG recordings.
Abstract
Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution. Under such circumstances, self-supervised learning (SSL) methods are highly effective for utilizing large datasets, making them a popular choice for electrocardiogram (ECG) analysis. This work presents the Event Reconstruction Joint-Embedding Predictive Architecture (ER-JEPA), a lightweight SSL framework for multivariate time series, whose name and two-fold hierarchical structure are inspired by the diagnostic approach of cardiologists. At its core, ER-JEPA features: (1) a two-stage structure that constructs representations for each time interval and subsequently processes these representations as a univariate time series, (2) the hierarchical integration of two Joint-Embedding Predictive Architectures (JEPAs), and (3) a Vision Transformer (ViT) backbone. The structural concatenation of two JEPAs categorizes the model as a Hierarchical JEPA (H-JEPA), designed to encode multiple levels of abstract representations for enhanced prediction on complex tasks. This study reports a successful application of H-JEPA to 12-lead ECG data as a multivariate time series, alongside an analysis of the sensitivity of hierarchical representation during the pretraining stage. Furthermore, this study provides a qualitative demonstration that the intermediate representations produced by the first module of ER-JEPA excel at local feature extraction, as they are structurally free from over-smoothing. Pretrained on approximately 180,000 10-second recordings, the model achieves state-of-the-art downstream performance on the ST-MEM benchmark, with rapid computation and minimal resource usage.
29 pages, 8 figures. Further clarified, added the new downstream task in the abstract and intro since last update.<< Polished text, improved formatting, fixed speed benchmark result, and added new downstream task. Code will be made publicly available soon