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

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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.

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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

ECGQuest: Benchmarking and Fine-Tuning Language Models for Electrocardiography

ECG-LLM: Foundation Model for ECG-Based Cardiac Reasoning