🤖 AI Summary
This survey addresses the fragmented research landscape, inconsistent evaluation practices, and lack of standardized benchmarks for deep generative modeling of physiological signals (ECG, EEG, PPG, EMG). Following the PRISMA guidelines, we conduct the first systematic review in this domain, integrating bibliometric analysis with cross-modal technical categorization to comprehensively synthesize works published between 2018 and 2023. Our method yields a structured knowledge graph covering model architectures, publicly available datasets, evaluation metrics, and clinical application scenarios. We clarify the technical evolution trajectory, identify core challenges—including data scarcity, inter-modal heterogeneity, and non-comparable evaluations—and propose a standardized benchmarking framework. The findings provide theoretical foundations and practical guidance for algorithm development, reproducible experimentation, and clinical translation of generative models in physiological signal analysis.
📝 Abstract
In this paper, we present a systematic literature review on deep generative models for physiological signals, particularly electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG) and electromyogram (EMG). Compared to the existing review papers, we present the first review that summarizes the recent state-of-the-art deep generative models. By analyzing 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.
