A Usable GAN-Based Tool for Synthetic ECG Generation in Cardiac Amyloidosis Research

Francesco Speziale, Ugo Lomoio, Fabiola Boccuto, Pierangelo Veltri, Pietro Hiram Guzzi
1/13/2026
cs.LG

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Abstract

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.

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Cite this paper

@article{speziale2026usable,
  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},
  year   = {2026},
  eprint = {2601.08260},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.08260v1}
}

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