# Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification

arXiv: https://arxiv.org/abs/2004.05399 · HTML: https://mlsanity.com/paper/2004.05399/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Explainable AI for Time Series via Virtual Inspection Layers](https://mlsanity.com/paper/2303.06365/) (2023)
- [LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification](https://mlsanity.com/paper/2207.12381/) (2022)
- [Improving ECG Classification Interpretability using Saliency Maps](https://mlsanity.com/paper/2201.04070/) (2022)
- [MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classification](https://mlsanity.com/paper/2306.12098/) (2023)
- [IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification](https://mlsanity.com/paper/2204.05116/) (2022)
- [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)
- [Cardi-GPT: An Expert ECG-Record Processing Chatbot](https://mlsanity.com/paper/2510.24737/) (2025)

## Abstract (arXiv)

Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering from cardiac arrhythmia. Traditionally, diagnosis is performed by examination of the Electrocardiogram (ECG) by a cardiologist. This method of diagnosis is hampered by the lack of accessibility to expert cardiologists. For quite some time, signal processing methods had been used to automate arrhythmia diagnosis. However, these traditional methods require expert knowledge and are unable to model a wide range of arrhythmia. Recently, Deep Learning methods have provided solutions to performing arrhythmia diagnosis at scale. However, the black-box nature of these models prohibit clinical interpretation of cardiac arrhythmia. There is a dire need to correlate the obtained model outputs to the corresponding segments of the ECG. To this end, two methods are proposed to provide interpretability to the models. The first method is a novel application of Gradient-weighted Class Activation Map (Grad-CAM) for visualizing the saliency of the CNN model. In the second approach, saliency is derived by learning the input deletion mask for the LSTM model. The visualizations are provided on a model whose competence is established by comparisons against baselines. The results of model saliency not only provide insight into the prediction capability of the model but also aligns with the medical literature for the classification of cardiac arrhythmia.