In this paper, we present a systematic literature review on deep generative models for physiological signals, particularly electrocardiogram, electroencephalogram, photoplethysmogram and electromyogram. Compared to the existing review papers, we present the first review that summarizes the recent state-of-the-art deep generative models. By analysing the state-of-the-art research related to deep generative models along with their main applications and challenges, this review contributes to the overall understanding of these models applied to physiological signals. Additionally, by highlighting the employed evaluation protocol and the most used physiological databases, this review facilitates the assessment and benchmarking of deep generative models.

翻译：本文对生理信号（特别是心电图、脑电图、光电容积脉搏波图和肌电图）的深度生成模型进行了系统性文献综述。与现有综述论文相比，本文首次总结了近期最先进的深度生成模型。通过分析与深度生成模型相关的最新研究及其主要应用和挑战，本综述有助于全面理解这些模型在生理信号中的应用。此外，通过强调所采用的评估方案和最常用的生理数据库，本综述促进了深度生成模型的评估与基准测试。