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

• URL: http://arxiv.org/abs/2601.15326v1
• Date: Mon, 19 Jan 2026 07:55:23 GMT
• Title: ECGomics: An Open Platform for AI-ECG Digital Biomarker Discovery
• Authors: Deyun Zhang, Jun Li, Shijia Geng, Yue Wang, Shijie Chen, Sumei Fan, Qinghao Zha, Shenda Hong,
• Abstract summary: ECGomics is a systematic paradigm for the multidimensional deconstruction of cardiac signals into digital biomarker.<n>We operationalized this framework into a scalable ecosystem consisting of a web-based research platform and a mobile-integrated solution.
• Score: 20.629258029871366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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