ECGomics: An Open Platform for AI-ECG Digital Biomarker Discovery

Background: Conventional electrocardiography (ECG) analysis faces a persistent dichotomy: Expert-defined features provide interpretability but are limited in capturing latent high-dimensional patterns, whereas deep learning approaches achieve strong predictive performance but often lack interpretability and require large annotated datasets. A systematic framework that integrates these paradigms remains needed. Methods: We propose ECGomics, a structured and deployable analytical paradigm that deconstructs cardiac electrical signals into 4 interconnected dimensions: Structural, Intensity, Functional, and Comparative. This taxonomy integrates expert-defined morphological metrics with artificial intelligence-derived latent embeddings to generate multidimensional digital biomarkers. Results: We operationalized this framework into a scalable ecosystem consisting of a web-based platform, a mobile solution (https://github.com/PKUDigitalHealth/ECGomics), and application programming interface invocation. Across multiple representative clinical scenarios-including atrial fibrillation detection, recurrence prediction after cryoablation, screening of severe coronary stenosis in apparently normal ECGs, and maternal cardiac monitoring-ECGomics demonstrated robust predictive performance while maintaining interpretability and relatively low data requirements. These results validate the flexibility and effectiveness of the proposed multidimensional framework. Conclusion: ECGomics establishes an omics-level representation system for ECG analysis, bridging conventional feature engineering and deep learning within a unified taxonomy. By providing a deployable digital biomarker ecosystem, this framework advances scalable precision cardiovascular assessment and data-driven health management.