# Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery

arXiv: https://arxiv.org/abs/2305.17043 · HTML: https://mlsanity.com/paper/2305.17043/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 78 references that are in the map.
- First posted on arXiv 26 May 2023.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [Explaining by Removing: A Unified Framework for Model Explanation](https://mlsanity.com/paper/2011.14878/) (2020)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01602/) (2022)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation](https://mlsanity.com/paper/2206.03208/) (2022)
- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [Self-supervised representation learning from 12-lead ECG data](https://mlsanity.com/paper/2103.12676/) (2021)
- [Concept Activation Regions: A Generalized Framework For Concept-Based Explanations](https://mlsanity.com/paper/2209.11222/) (2022)
- [Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy](https://mlsanity.com/paper/2106.13200/) (2021)
- [Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees](https://mlsanity.com/paper/2301.11911/) (2023)
- [Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data](https://mlsanity.com/paper/1904.07032/) (2019)
- [Detecting and interpreting myocardial infarction using fully convolutional neural networks](https://mlsanity.com/paper/1806.07385/) (2018)
- [Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery](https://mlsanity.com/paper/1807.02569/) (2018)
- [Analysis of a Deep Learning Model for 12-Lead ECG Classification Reveals Learned Features Similar to Diagnostic Criteria](https://mlsanity.com/paper/2211.01738/) (2022)
- [Improving ECG Classification Interpretability using Saliency Maps](https://mlsanity.com/paper/2201.04070/) (2022)
- [Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models](https://mlsanity.com/paper/2211.07579/) (2022)
- [Using Deep Networks for Scientific Discovery in Physiological Signals](https://mlsanity.com/paper/2008.10936/) (2020)

## Built on by (most cited first, 15 of 15)

- [Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care](https://mlsanity.com/paper/2407.17856/) (2024)
- [Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study](https://mlsanity.com/paper/2502.19167/) (2025)
- [Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches](https://mlsanity.com/paper/2502.19949/) (2025)
- [Using explainable AI to investigate electrocardiogram changes during healthy aging -- from expert features to raw signals](https://mlsanity.com/paper/2310.07463/) (2023)
- [Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features](https://mlsanity.com/paper/2408.17329/) (2024)
- [Abnormality Prediction and Forecasting of Laboratory Values from Electrocardiogram Signals Using Multimodal Deep Learning](https://mlsanity.com/paper/2411.14886/) (2024)
- [Glocal Explanations of Expected Goal Models in Soccer](https://mlsanity.com/paper/2308.15559/) (2023)
- [Explaining Time Series Classification Predictions via Causal Attributions](https://mlsanity.com/paper/2405.15871/) (2024)
- [Ensuring Medical AI Safety: Interpretability-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data](https://mlsanity.com/paper/2501.13818/) (2025)
- [Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care](https://mlsanity.com/paper/2312.11050/) (2023)
- [Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG](https://mlsanity.com/paper/2603.21832/) (2026)
- [AICRN: Attention-Integrated Convolutional Residual Network for Interpretable Electrocardiogram Analysis](https://mlsanity.com/paper/2508.12162/) (2025)
- [Associations between echocardiographic traits and AI-ECG predictions of heart failure](https://mlsanity.com/paper/2605.24576/) (2026)
- [MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection](https://mlsanity.com/paper/2606.06718/) (2026)
- [Beyond Local Inspection: Global, Guideline-Grounded Evaluation of Post-hoc XAI Methods for ECG Classification](https://mlsanity.com/paper/2607.24035/) (2026)

## Abstract (arXiv)

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 local (attributions per sample) and global (based on domain expert concepts) perspectives. We have established a set of sanity checks to identify sensible attribution methods, and we provide quantitative evidence in accordance with expert rules. This dataset-wide analysis goes beyond anecdotal evidence by aggregating data across patient subgroups. 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.