# LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification

arXiv: https://arxiv.org/abs/2207.12381 · HTML: https://mlsanity.com/paper/2207.12381/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 77 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [On the (In)fidelity and Sensitivity for Explanations](https://mlsanity.com/paper/1901.09392/) (2019)
- [An Attentive Survey of Attention Models](https://mlsanity.com/paper/1904.02874/) (2019)
- [Designing ECG Monitoring Healthcare System with Federated Transfer Learning and Explainable AI](https://mlsanity.com/paper/2105.12497/) (2021)
- [Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review](https://mlsanity.com/paper/2001.01550/) (2019)
- [Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram](https://mlsanity.com/paper/2010.10328/) (2020)
- [Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification](https://mlsanity.com/paper/2004.05399/) (2020)
- [COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network](https://mlsanity.com/paper/2106.00436/) (2021)

## Built on by (most cited first, 11 of 11)

- [Explainable Artificial Intelligence for Medical Applications: A Review](https://mlsanity.com/paper/2412.01829/) (2024)
- [Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks](https://mlsanity.com/paper/2208.05433/) (2022)
- [A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility](https://mlsanity.com/paper/2503.07276/) (2025)
- [A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals](https://mlsanity.com/paper/2208.03408/) (2022)
- [ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification](https://mlsanity.com/paper/2505.03787/) (2025)
- [Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration](https://mlsanity.com/paper/2208.07088/) (2022)
- [MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification](https://mlsanity.com/paper/2311.15041/) (2023)
- [ECG Arrhythmia Detection Using Disease-specific Attention-based Deep Learning Model](https://mlsanity.com/paper/2407.18033/) (2024)
- [EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification](https://mlsanity.com/paper/2506.12404/) (2025)
- [Explainable Deep Learning-based Classification of Wolff-Parkinson-White Electrocardiographic Signals](https://mlsanity.com/paper/2511.05973/) (2025)
- [Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals](https://mlsanity.com/paper/2605.27583/) (2026)

## Abstract (arXiv)

Cardiovascular diseases (CVDs) are a group of heart and blood vessel disorders that is one of the most serious dangers to human health, and the number of such patients is still growing. Early and accurate detection plays a key role in successful treatment and intervention. Electrocardiogram (ECG) is the gold standard for identifying a variety of cardiovascular abnormalities. In clinical practices and most of the current research, standard 12-lead ECG is mainly used. However, using a lower number of leads can make ECG more prevalent as it can be conveniently recorded by portable or wearable devices. In this research, we develop a novel deep learning system to accurately identify multiple cardiovascular abnormalities by using only three ECG leads.