2026-02-23: xGNN4MI
In this study, we introduce xGNN4MI, an open-source framework for graph-based modelling of 12-lead ECG signals. By explicitly encoding spatial relationships between ECG leads and their temporal structure, the framework provides a transparent and reproducible pipeline for GNN-based cardiovascular disease classification.
https://www.uni-giessen.de/en/faculties/f11/departments/predlmed/news/pub_xgnn4mi
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2026-02-23: xGNN4MI
In this study, we introduce xGNN4MI, an open-source framework for graph-based modelling of 12-lead ECG signals. By explicitly encoding spatial relationships between ECG leads and their temporal structure, the framework provides a transparent and reproducible pipeline for GNN-based cardiovascular disease classification.
In this study, we introduce xGNN4MI, an open-source framework for graph-based modelling of 12-lead ECG signals. By explicitly encoding spatial relationships between ECG leads and their temporal structure, the framework provides a transparent and reproducible pipeline for GNN-based cardiovascular disease classification.
Key Highlights:
- Integrated explainability workflow
- physiologically meaningful insights
- external validation on population-based cohort
- open-source and reproducible
📖 Read the full paper: https://lnkd.in/eeyxrdmh
Big thanks to to my co-authors and collaborators at Department of Medical Informatics, University Medical Center, CIDAS University of Göttingen, Department of Artificial Intelligence in Biomedical Engineering, University Erlangen-Nürnberg, Institute of Bioinformatics, University Medicine Greifswald, Department of Health Technology, Technical University of Denmark, PredLmed Gießen for their valuable contributions. 🙏
Miriam Cindy Maurer, Philip Hempel, Kristin Steinhaus, Hryhorii Chereda, Marcus Vollmer, Dagmar Krefting, Nicolai Spicher and Anne-Christin Hauschild