# ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2107.07677 · HTML: https://mlsanity.com/paper/2107.07677/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models](https://mlsanity.com/paper/1805.06605/) (2018)
- [MagNet: a Two-Pronged Defense against Adversarial Examples](https://mlsanity.com/paper/1705.09064/) (2017)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Semi-Supervised Learning with Generative Adversarial Networks](https://mlsanity.com/paper/1606.01583/) (2016)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)
- [Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks](https://mlsanity.com/paper/1707.01836/) (2017)
- [VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers](https://mlsanity.com/paper/2104.06757/) (2021)
- [Synthesis of Realistic ECG using Generative Adversarial Networks](https://mlsanity.com/paper/1909.09150/) (2019)
- [Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach](https://mlsanity.com/paper/1812.07421/) (2018)
- [Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes](https://mlsanity.com/paper/2004.00306/) (2020)
- [ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System](https://mlsanity.com/paper/1901.03808/) (2019)

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

- [DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports and Patient-Specific Information](https://mlsanity.com/paper/2501.05932/) (2025)
- [ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases](https://mlsanity.com/paper/2207.10670/) (2022)
- [GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction](https://mlsanity.com/paper/2208.01220/) (2022)
- [Deep Generative Models for Physiological Signals: A Systematic Literature Review](https://mlsanity.com/paper/2307.06162/) (2023)
- [Personalized Heart Disease Detection via ECG Digital Twin Generation](https://mlsanity.com/paper/2404.11171/) (2024)
- [ECG-ATK-GAN: Robustness against Adversarial Attacks on ECGs using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2110.09983/) (2021)
- [Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report](https://mlsanity.com/paper/2304.06286/) (2023)
- [On the Computational Entanglement of Distant Features in Adversarial Machine Learning](https://mlsanity.com/paper/2309.15669/) (2023)

## Abstract (arXiv)

Electrocardiogram (ECG) acquisition requires an automated system and analysis pipeline for understanding specific rhythm irregularities. Deep neural networks have become a popular technique for tracing ECG signals, outperforming human experts. Despite this, convolutional neural networks are susceptible to adversarial examples that can misclassify ECG signals and decrease the model's precision. Moreover, they do not generalize well on the out-of-distribution dataset. The GAN architecture has been employed in recent works to synthesize adversarial ECG signals to increase existing training data. However, they use a disjointed CNN-based classification architecture to detect arrhythmia. Till now, no versatile architecture has been proposed that can detect adversarial examples and classify arrhythmia simultaneously. To alleviate this, we propose a novel Conditional Generative Adversarial Network to simultaneously generate ECG signals for different categories and detect cardiac abnormalities. Moreover, the model is conditioned on class-specific ECG signals to synthesize realistic adversarial examples. Consequently, we compare our architecture and show how it outperforms other classification models in normal/abnormal ECG signal detection by benchmarking real world and adversarial signals.