在基于 GAN 的 ECG 合成中利用统计形状先验
Leveraging Statistical Shape Priors in GAN-based ECG Synthesis
AI总结：
本文提出结合 GAN 与统计形状先验的 ECG 信号生成方法，将时间与幅度变化建模为二维形状，并在 MIT-BIH 上验证其优于现有 GAN 基线。
摘要：
紧急情况下的心电图（ECG）数据采集颇具挑战，因此 ECG 数据生成成为处理高度不平衡 ECG 训练数据集的一种有效解决方案。本文提出一种结合生成对抗网络（GAN）与统计 ECG 数据建模的新型 ECG 信号生成方法。该方法利用关于 ECG 动力学的先验知识合成逼真信号，以应对 ECG 信号的复杂动力学。为验证该方法，作者使用来自 MIT-BIH 心律失常数据库的 ECG 信号开展实验。结果表明，该方法将 ECG 信号的时间和幅度变化建模为二维形状，与最先进的基于 GAN 的生成基线相比，能够生成更逼真的信号。所提方法对提升 ECG 训练数据集质量具有重要意义，最终可改善 ECG 分类算法的性能。本研究有助于开发更高效、更准确的 ECG 分析方法，从而辅助心脏疾病的诊断与治疗。
英文摘要：
Electrocardiogram (ECG) data collection during emergency situations is challenging, making ECG data generation an efficient solution for dealing with highly imbalanced ECG training datasets. In this paper, we propose a novel approach for ECG signal generation using Generative Adversarial Networks (GANs) and statistical ECG data modeling. Our approach leverages prior knowledge about ECG dynamics to synthesize realistic signals, addressing the complex dynamics of ECG signals. To validate our approach, we conducted experiments using ECG signals from the MIT-BIH arrhythmia database. Our results demonstrate that our approach, which models temporal and amplitude variations of ECG signals as 2-D shapes, generates more realistic signals compared to state-of-the-art GAN based generation baselines. Our proposed approach has significant implications for improving the quality of ECG training datasets, which can ultimately lead to better performance of ECG classification algorithms. This research contributes to the development of more efficient and accurate methods for ECG analysis, which can aid in the diagnosis and treatment of cardiac diseases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
