arXiv Machine Learning July 31, 2026 By Yixuan Duan, Wei Qiu

ECG-InterpBench: Benchmarking the Interpretability of ECG Foundation Models with Matched-Scale Sparse Autoencoders

Read the original on arXiv Machine Learning →

arXiv:2607. 27404v1 Announce Type: new Abstract: Existing benchmarks for electrocardiogram foundation models primarily evaluate downstream predictive performance, providing limited insight into whether their internal representations can be faithfully decomposed, clinically interpreted, or reproduced across independent analyses.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Related stories

FOUND-AF: Benchmarking ECG Foundation Models for Atrial Fibrillation Detection

Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction

ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation

CADENCE: A Cardiac Atom Dictionary for Interpretable Neural Concept Extraction from ECG Foundation Models

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