  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.
