  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%.
