Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
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Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the glocal (aggregated local attributions over multiple samples) and global (concept based XAI) perspectives. We have established a set of sanity checks to identify saliency as the most sensible attribution method. We provide a dataset-wide analysis across entire patient subgroups, which goes beyond anecdotal evidence, to establish the first quantitative evidence for the alignment of model behavior with cardiologists' decision rules. Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction. We believe that these proposed methods can serve as building blocks for a complementary assessment of the internal validity during a certification process, as well as for knowledge discovery in the field of ECG analysis.
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Other work has used broader explainability frameworks to compare model attention with known cardiologist decision rules and to investigate whether deep learning models use clinically plausible ECG features.
“Other work has used broader explainability frameworks to compare model attention with known cardiologist decision rules and to investigate whether deep learning models use clinically plausible ECG features [49].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [49]
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- Authors
- Wagner, P., Mehari, T., Haverkamp, W., & Strodthoff, N.
- Affiliations
- Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (DE)
- Physikalisch-Technische Bundesanstalt (DE)
- Charité – Universitätsmedizin Berlin (DE)
- Carl von Ossietzky Universität Oldenburg (DE)
- Corresponding author
- Strodthoff, Nils
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 176
- Pages
- 108525
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W4396670015
- Cited by
- 48
- References
- 70
- Citations by year
- 2026: 18 · 2025: 20 · 2024: 9 · 2023: 1
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Carl von Ossiezky University of Oldenburg (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- Funders
- Senat von Berlin
- European Commission
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- explainable AI, ECG signal analysis, deep neural network, saliency maps, global explanations, model interpretability, knowledge discovery, model auditing, model certification
- MeSH terms
- Deep Learning, Electrocardiography, Humans, Signal Processing, Computer-Assisted, Neural Networks, Computer, Knowledge Discovery
About these checks
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