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.