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arXiv · 2601.08260

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

Francesco Speziale·Ugo Lomoio·Fabiola Boccuto·Pierangelo Veltri·Pietro Hiram Guzzi
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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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Francesco Speziale, Ugo Lomoio, Fabiola Boccuto, Pierangelo Veltri, Pietro Hiram Guzzi. 2026-01-13. A Usable GAN-Based Tool for Synthetic ECG Generation in Cardiac Amyloidosis Research. https://arxiv.org/abs/2601.08260

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