Cardiac amyloidosis (CA) is a rare and underdiagnosed infiltrative cardiomyopathy, and available datasets for machine-learning models are typically small, imbalanced and heterogeneous. This paper presents a Generative Adversarial Network (GAN) and a graphical command-line interface for generating realistic synthetic electrocardiogram (ECG) beats to support early diagnosis and patient stratification in CA. The tool is designed for usability, allowing clinical researchers to train class-specific generators once and then interactively produce large volumes of labelled synthetic beats that preserve the distribution of minority classes.
@article{arxiv.2601.08260,
title = {A Usable GAN-Based Tool for Synthetic ECG Generation in Cardiac Amyloidosis Research},
author = {Francesco Speziale and Ugo Lomoio and Fabiola Boccuto and Pierangelo Veltri and Pietro Hiram Guzzi},
journal= {arXiv preprint arXiv:2601.08260},
year = {2026}
}