Lightweight self-supervised learning for multivariate time series with ER-JEPA in ECG

The analysis of biomedical signals, such as electrocardiograms, presents a common challenge: having small labeled datasets alongside large volumes of unannotated recordings. To address this asymmetry, self-supervised learning has become an effective strategy, allowing the extraction of meaningful representations from unlabeled data. In this context, the ER-JEPA (Event Reconstruction Joint-Embedding Predictive Architecture) architecture represents a significant advancement, offering a lightweight and hierarchical framework for multivariate time series. Its design, inspired by the sequential reasoning of cardiologists, combines two predictive encoding stages that generate abstract representations at multiple levels, optimizing performance in complex tasks with minimal resource consumption. This type of innovation not only boosts diagnostic accuracy but also opens possibilities for integration into enterprise solutions of AI for businesses that require efficiently processing large streams of sensory data. At Q2BSTUDIO, we develop custom applications that incorporate artificial intelligence models, AI agents, and deep learning techniques tailored to each organization's specific needs. Our experience ranges from implementing AWS and Azure cloud services to cybersecurity and business intelligence services with tools like Power BI, all aimed at turning complex data into strategic decisions. Thus, architectures like ER-JEPA find a natural path toward production environments where computational efficiency and generalization capability are critical. The combination of custom software with lightweight self-supervision models makes it possible to tackle real-world challenges in sectors such as healthcare, industry, or finance, maximizing the value of available data without relying on costly manual labeling.