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ECGomics integrates expert ECG analysis with AI to create multidimensional digital biomarkers. This novel framework enhances cardiovascular assessment accuracy and interpretability, even with limited data.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Conventional electrocardiography (ECG) analysis struggles to balance expert interpretability with deep learning's predictive power.
  • A need exists for a systematic framework integrating these approaches for comprehensive ECG analysis.

Purpose of the Study:

  • To introduce ECGomics, a structured paradigm for ECG analysis.
  • To bridge traditional feature engineering with deep learning using a unified taxonomy.

Main Methods:

  • Deconstructing cardiac electrical signals into Structural, Intensity, Functional, and Comparative dimensions.
  • Integrating expert-defined metrics with AI-derived latent embeddings to create digital biomarkers.
  • Developing a scalable ecosystem including web, mobile, and API solutions.

Main Results:

  • ECGomics demonstrated robust predictive performance across diverse clinical scenarios (e.g., atrial fibrillation, coronary stenosis).
  • The framework maintained interpretability and required relatively low data.
  • Validated flexibility and effectiveness in cardiovascular assessment.

Conclusions:

  • ECGomics establishes an omics-level ECG representation system.
  • Advances scalable precision cardiovascular assessment and data-driven health management.
  • Provides a deployable digital biomarker ecosystem.