用GAN生成逼真心电图，助力罕见心脏病早期诊断。
A Usable GAN-Based Tool for Synthetic ECG Generation in Cardiac Amyloidosis Research
- 基于GAN构建可交互的心电图生成工具，支持按类别生成
- 能生成大量标注数据，有效保留少数类分布特征
- 专为临床研究设计，无需深度学习背景也可使用
心脏淀粉样变性（CA）是一种罕见且易被漏诊的浸润性心肌病，现有用于机器学习模型的数据集通常规模小、分布不均且异质性强。本文提出一种生成对抗网络（GAN）及图形化命令行界面，用于生成真实感强的合成心电图波形，以支持CA的早期诊断与患者分层。该工具注重可用性，临床研究人员只需训练一次特定类别的生成器，即可交互式生成大量带标签的合成波形，且能保持少数类别的分布特性。
原文摘要 · Abstract (English)
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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