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

2026-01-13

AI Summary

This study addresses the limitations in cardiac amyloidosis (CA) research imposed by small-scale, class-imbalanced, and heterogeneous electrocardiogram (ECG) datasets. To overcome these challenges, the authors propose an interactive generative adversarial network (GAN)-based tool that enables clinical researchers to train class-specific generators on demand, efficiently synthesizing high-quality, label-preserving ECG beats. The approach faithfully reconstructs the underlying data distribution, substantially alleviating data scarcity and class imbalance in CA. Furthermore, a graphical command-line interface enhances usability, offering a scalable data augmentation solution to support early diagnosis and patient stratification in cardiac amyloidosis.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

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.

Problem

• cardiac amyloidosis
• synthetic ECG generation
• imbalanced datasets
• rare disease
• data scarcity

Innovation

• Generative Adversarial Network
• Synthetic ECG
• Cardiac Amyloidosis
• Class-Imbalanced Data
• Usable AI Tool