Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy
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Computer Science > Machine Learning
Title:Quantifying Explainable AI-introduced signal noise on ECG data with Spectral Entropy
Abstract:Explainability techniques are used to assess the output of various deep learning models. This is especially true in healthcare, where models need to be trusted and decisions justified. Explainability (XAI) tools use heuristics which often add signal noise to the explanation "core". It is not always obvious what is signal from the model and what is noise from the XAI. We propose the use of spectral entropy as a measure of noise in XAI output. We demonstrate its usefulness in the context of classifying arrhythmias in an ECG dataset with different post hoc explainability techniques.
| Comments: | Accepted to EUSIPCO 2026 |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.24974 [cs.LG] |
| (or arXiv:2606.24974v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24974
arXiv-issued DOI via DataCite
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