# Improving ECG Classification Interpretability using Saliency Maps

arXiv: https://arxiv.org/abs/2201.04070 · HTML: https://mlsanity.com/paper/2201.04070/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Detecting and interpreting myocardial infarction using fully convolutional neural networks](https://mlsanity.com/paper/1806.07385/) (2018)
- [Expert-Augmented Machine Learning](https://mlsanity.com/paper/1903.09731/) (2019)
- [Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification](https://mlsanity.com/paper/2004.05399/) (2020)

## Built on by (most cited first, 9 of 9)

- [Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery](https://mlsanity.com/paper/2305.17043/) (2023)
- [Pretraining ECG Data with Adversarial Masking Improves Model Generalizability for Data-Scarce Tasks](https://mlsanity.com/paper/2211.07889/) (2022)
- [Interpretable Multimodal Zero-Shot ECG Diagnosis via Structured Clinical Knowledge Alignment](https://mlsanity.com/paper/2510.21551/) (2025)
- [Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities](https://mlsanity.com/paper/2308.08407/) (2023)
- [Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data](https://mlsanity.com/paper/2205.02803/) (2022)
- [ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction](https://mlsanity.com/paper/2405.09567/) (2024)
- [Evaluating Feature Attribution Methods for Electrocardiogram](https://mlsanity.com/paper/2211.12702/) (2022)
- [Consensus of state of the art mortality prediction models: From all-cause mortality to sudden death prediction](https://mlsanity.com/paper/2308.16067/) (2023)
- [Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals](https://mlsanity.com/paper/2511.05973/) (2025)

## Abstract (arXiv)

Cardiovascular disease is a large worldwide healthcare issue; symptoms often present suddenly with minimal warning. The electrocardiogram (ECG) is a fast, simple and reliable method of evaluating the health of the heart, by measuring electrical activity recorded through electrodes placed on the skin. ECGs often need to be analyzed by a cardiologist, taking time which could be spent on improving patient care and outcomes. Because of this, automatic ECG classification systems using machine learning have been proposed, which can learn complex interactions between ECG features and use this to detect abnormalities. However, algorithms built for this purpose often fail to generalize well to unseen data, reporting initially impressive results which drop dramatically when applied to new environments. Additionally, machine learning algorithms suffer a "black-box" issue, in which it is difficult to determine how a decision has been made. This is vital for applications in healthcare, as clinicians need to be able to verify the process of evaluation in order to trust the algorithm. This paper proposes a method for visualizing model decisions across each class in the MIT-BIH arrhythmia dataset, using adapted saliency maps averaged across complete classes to determine what patterns are being learned. We do this by building two algorithms based on state-of-the-art models. This paper highlights how these maps can be used to find problems in the model which could be affecting generalizability and model performance. Comparing saliency maps across complete classes gives an overall impression of confounding variables or other biases in the model, unlike what would be highlighted when comparing saliency maps on an ECG-by-ECG basis.