基于变分自编码器的合成电图生成用于心电图成像
Synthetic Electrogram Generation with Variational Autoencoders for ECGI
AI总结：
本文提出基于变分自编码器生成合成心房电图，用于解决ECGI中数据稀缺问题，通过两种模型提升电图重建性能。
摘要：
心房颤动（AF）是最常见的持续性心律失常，其临床评估需要准确表征心房的电活动。非侵入性心电图成像（ECGI）结合深度学习（DL）方法用于从体表电位（BSPMs）估计心内电图（EGMs）已显示出前景，但进展受配对BSPM-EGM数据集有限的阻碍。为了解决这一限制，我们研究了变分自编码器（VAEs）用于生成合成多通道心房EGMs。提出了两种模型：一种特定于窦性节律的VAE（VAE-S）和一种基于类别的VAE（VAE-C），该模型在窦性节律和AF信号上进行训练。生成的EGMs通过形态学、频谱和分布相似性度量进行评估。VAE-S在计算机模拟的EGMs中具有更高的保真度，而VAE-C在牺牲窦性重建质量的情况下实现了节律特定的生成。作为概念验证，生成的EGMs用于数据增强在下游非侵入性EGM重建任务中，其中中等增强提高了估计性能。这些结果展示了基于VAE的生成建模的潜力，可以缓解数据稀缺并增强基于深度学习的ECGI管道。
英文摘要：
Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, and its clinical assessment requires accurate characterization of atrial electrical activity. Noninvasive electrocardiographic imaging (ECGI) combined with deep learning (DL) approaches for estimating intracardiac electrograms (EGMs) from body surface potentials (BSPMs) has shown promise, but progress is hindered by the limited availability of paired BSPM-EGM datasets. To address this limitation, we investigate variational autoencoders (VAEs) for the generation of synthetic multichannel atrial EGMs. Two models are proposed: a sinus rhythm-specific VAE (VAE-S) and a class-conditioned VAE (VAE-C) trained on both sinus rhythm and AF signals. Generated EGMs are evaluated using morphological, spectral, and distributional similarity metrics. VAE-S achieves higher fidelity with respect to in silico EGMs, while VAE-C enables rhythm-specific generation at the expense of reduced sinus reconstruction quality. As a proof of concept, the generated EGMs are used for data augmentation in a downstream noninvasive EGM reconstruction task, where moderate augmentation improves estimation performance. These results demonstrate the potential of VAE-based generative modeling to alleviate data scarcity and enhance deep learning-based ECGI pipelines.
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