# Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search

arXiv: https://arxiv.org/abs/2301.10173 · HTML: https://mlsanity.com/paper/2301.10173/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 72 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Searching for A Robust Neural Architecture in Four GPU Hours](https://mlsanity.com/paper/1910.04465/) (2019)
- [Adversarial Audio Synthesis](https://mlsanity.com/paper/1802.04208/) (2018)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://mlsanity.com/paper/2006.05132/) (2020)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [Best Practices for Scientific Research on Neural Architecture Search](https://mlsanity.com/paper/1909.02453/) (2019)
- [Synthesis of Realistic ECG using Generative Adversarial Networks](https://mlsanity.com/paper/1909.09150/) (2019)
- [TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation](https://mlsanity.com/paper/2206.13676/) (2022)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Synthetic ECG Signal Generation Using Generative Neural Networks](https://mlsanity.com/paper/2112.03268/) (2021)
- [Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1805.09133/) (2018)
- [Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search](https://mlsanity.com/paper/2105.00693/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Data](https://mlsanity.com/paper/2411.12274/) (2024)

## Abstract (arXiv)

This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF), a pathological characteristic of Electrocardiogram (ECG) that can lead to fatal conditions such as heart attack. To enhance the learning process, the framework involves a Generative Adversarial Network (GAN) along with a Neural Architecture Search (NAS) in the data preparation and classifier optimization phases. The GAN is innovatively invoked to overcome the class imbalance of the training data by producing the synthetic ECG for PxAF class in a certified manner. The effect of the certified GAN is statistically validated. Instead of using a general-purpose classifier, the NAS automatically designs a highly accurate convolutional neural network architecture customized for the PxAF classification task. Experimental results show that the accuracy of the proposed framework exhibits a high value of 99% which not only enhances state-of-the-art by up to 5.1%, but also improves the classification performance of the two widely-accepted baseline methods, ResNet-18, and Auto-Sklearn, by 2.2% and 6.1%.