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

2026-01-19

AI Summary

This study addresses the longstanding challenge in electrocardiogram (ECG) analysis of simultaneously achieving high accuracy through deep learning and interpretability via expert-derived features, while also overcoming data inefficiency. The authors propose ECGomics, a novel paradigm inspired by genomics that introduces a four-dimensional analytical framework—encompassing structure, amplitude, function, and comparative dimensions—to integrate morphological rule-based engines with deep embedding representations, thereby bridging the gap between handcrafted features and data-driven models. The work implements a dual-end deployment architecture supporting high-throughput processing, 12-lead visualization, and real-time mobile analysis. An open-source web research platform and mobile system are released, enabling high-fidelity signal input, precise parameter configuration, and automated generation of structured reports, thus facilitating the widespread adoption of professional-grade ECG monitoring in both clinical and home settings.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Application Domains: Mobility, Driving & Flight
• Computer Vision: Multi-modal Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Background: Conventional electrocardiogram (ECG) analysis faces a persistent dichotomy: expert-driven features ensure interpretability but lack sensitivity to latent patterns, while deep learning offers high accuracy but functions as a black box with high data dependency. We introduce ECGomics, a systematic paradigm and open-source platform for the multidimensional deconstruction of cardiac signals into digital biomarker. Methods: Inspired by the taxonomic rigor of genomics, ECGomics deconstructs cardiac activity across four dimensions: Structural, Intensity, Functional, and Comparative. This taxonomy synergizes expert-defined morphological rules with data-driven latent representations, effectively bridging the gap between handcrafted features and deep learning embeddings. Results: We operationalized this framework into a scalable ecosystem consisting of a web-based research platform and a mobile-integrated solution (https://github.com/PKUDigitalHealth/ECGomics). The web platform facilitates high-throughput analysis via precision parameter configuration, high-fidelity data ingestion, and 12-lead visualization, allowing for the systematic extraction of biomarkers across the four ECGomics dimensions. Complementarily, the mobile interface, integrated with portable sensors and a cloud-based engine, enables real-time signal acquisition and near-instantaneous delivery of structured diagnostic reports. This dual-interface architecture successfully transitions ECGomics from theoretical discovery to decentralized, real-world health management, ensuring professional-grade monitoring in diverse clinical and home-based settings. Conclusion: ECGomics harmonizes diagnostic precision, interpretability, and data efficiency. By providing a deployable software ecosystem, this paradigm establishes a robust foundation for digital biomarker discovery and personalized cardiovascular medicine.

Problem

• ECG analysis
• digital biomarker
• interpretability
• deep learning
• cardiac signals

Innovation

• ECGomics
• digital biomarker
• interpretable AI
• multidimensional ECG deconstruction
• open-source platform