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

TL;DR

This paper introduces a machine learning framework combining a certified GAN and neural architecture search to accurately detect Paroxysmal Atrial Fibrillation from ECG data, significantly outperforming existing methods.

Contribution

It presents a novel framework that uses a certified GAN for data augmentation and NAS for optimized classifier design, achieving state-of-the-art accuracy in PxAF detection.

Findings

Achieved 99% classification accuracy, surpassing previous methods.

Validated the effectiveness of the certified GAN in handling class imbalance.

Improved baseline classifiers' performance by up to 6.1%.

Abstract

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%.