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arXiv Machine Learning By Artur Xavier, Guilherme Henrique Gomes Evangelista, Aitazaz Khan, Antonio Luiz Pinho Ribeiro, Mohammed J. Zaki, Srinivasan Parthasarathy, Wagner Meira Jr

Accurate and Auditable ECG Classification via Visibility Graph Neural Networks

Read the original on arXiv Machine Learning →

The paper introduces LYNX-ECG, a graph neural network that represents each heartbeat as a sparse visibility graph of timestamped turning points, enabling accurate ECG classification while providing auditable, clinically grounded explanations. On the CODE-Test dataset, LYNX-ECG achieves a macro F1 score of 0.889, outperforming five baselines including the ECGFounder foundation model, and its graphs require only about 14% of the storage needed for full-sample graphs. Because each graph node corresponds to a specific ECG time, clinicians can delete waveform regions or alter beat timing to audit predictions, with the clinically expected region proving strongest evidence in over 90% of correct conduction disorder predictions. "whyItMatters":"The model’s ability to pair high classification accuracy with transparent, testable explanations directly addresses the need for clinically interpretable AI in ECG analysis."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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