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Accurate and Auditable ECG Classification via Visibility Graph Neural Networks — arXiv:2610.41795

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41795
License (as archived): CC0-1.0
Archived: 2026-11-02T07:10:47.496Z
SHA-256 of the archived text: 54ce743c4ac2255602f4e2e22938d2b67fb9021b35be3bbc92db9bf89dfb74fe
Stable URL of this page: https://metraipolis.com/documento/dc_e3bbbf93b60b

How to cite

Accurate and Auditable ECG Classification via Visibility Graph Neural Networks — arXiv:2610.41795 — arXiv (en), https://arxiv.org/abs/2610.41795 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e3bbbf93b60b (Metraipolis archive dc_e3bbbf93b60b, sha256 54ce743c4ac2255602f4e2e22938d2b67fb9021b35be3bbc92db9bf89dfb74fe)

BibTeX

@misc{dce3bbbf93b60b,
  title = {Accurate and Auditable ECG Classification via Visibility Graph Neural Networks — arXiv:2610.41795},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41795},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e3bbbf93b60b; sha256 54ce743c4ac2255602f4e2e22938d2b67fb9021b35be3bbc92db9bf89dfb74fe},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41795. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Physicians read electrocardiograms (ECGs) from waveform shape and timing, yet accurate deep-learning classifiers rarely show which waveform regions drive their predictions. We introduce LYNX-ECG, a graph neural network that represents each heartbeat as a sparse visibility graph of timestamped turning points. On CODE-Test, LYNX-ECG reaches a macro F1 score of 0.889, outperforming five baselines including the ECGFounder foundation model, while its graphs need about 14% of the storage of graphs built from every sample. Because each node maps to a specific ECG time, we can delete clinically defined waveform regions and alter beat timing to audit predictions. For conduction disorders, the clinically expected region was the strongest evidence in more than 90% of correct predictions. Bradycardia and tachycardia calls follow heart rate with conduction intervals held fixed, and atrial fibrillation calls vanish when beat-to-beat variation is removed. Sparse visibility graphs thus pair accurate ECG classification with clinically grounded, testable explanations.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41795 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:06:59.455Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).