A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG Data
arXiv:2607.01145v1 Announce Type: new 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 inspi
The increasing availability of large, unannotated medical datasets and the growing demand for efficient AI analysis in healthcare drives the development of such self-supervised learning frameworks.
This development allows for more effective utilization of vast medical data, improving diagnostic capabilities and potentially reducing the reliance on costly, human-annotated datasets in critical health applications.
Healthcare AI development becomes more efficient and accessible, particularly for specialized applications like ECG analysis where labelled data is scarce but raw data is abundant.
- · Medical AI developers
- · Healthcare providers
- · Patients (through improved diagnostics)
- · AI research institutions
- · Companies relying solely on supervised learning for medical data
- · Businesses specializing in manual medical data annotation
Self-supervised learning for multivariate time series in healthcare becomes more sophisticated and widespread.
Improved and more accessible diagnostic tools for cardiovascular conditions emerge due to better AI models.
The overall cost and time required for developing and deploying AI solutions in medical diagnostics significantly decrease, accelerating innovation in the sector.
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Read at arXiv cs.LG