# Deep Generative Models for Physiological Signals: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2307.06162 · HTML: https://mlsanity.com/paper/2307.06162/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 105 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 28 of 28)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models](https://mlsanity.com/paper/2208.09399/) (2022)
- [Diffusion-based Conditional ECG Generation with Structured State Space Models](https://mlsanity.com/paper/2301.08227/) (2023)
- [CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG](https://mlsanity.com/paper/2010.00104/) (2020)
- [SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification](https://mlsanity.com/paper/2006.15353/) (2020)
- [Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data](https://mlsanity.com/paper/1905.00931/) (2019)
- [DeScoD-ECG: Deep Score-Based Diffusion Model for ECG Baseline Wander and Noise Removal](https://mlsanity.com/paper/2208.00542/) (2022)
- [Synthetic ECG Signal Generation using Probabilistic Diffusion Models](https://mlsanity.com/paper/2303.02475/) (2023)
- [EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders](https://mlsanity.com/paper/2207.08002/) (2022)
- [Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging](https://mlsanity.com/paper/1806.05452/) (2018)
- [Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience](https://mlsanity.com/paper/2206.07081/) (2022)
- [An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN](https://mlsanity.com/paper/2106.11512/) (2021)
- [DiffECG: A Versatile Probabilistic Diffusion Model for ECG Signals Synthesis](https://mlsanity.com/paper/2306.01875/) (2023)
- [Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder](https://mlsanity.com/paper/2002.00254/) (2020)
- [EEG to fMRI Synthesis: Is Deep Learning a candidate?](https://mlsanity.com/paper/2009.14133/) (2020)
- [ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2107.07677/) (2021)
- [EEG Synthetic Data Generation Using Probabilistic Diffusion Models](https://mlsanity.com/paper/2303.06068/) (2023)
- [Conditional Generation of Periodic Signals with Fourier-Based Decoder](https://mlsanity.com/paper/2110.12365/) (2021)
- [Arrhythmia Classification using CGAN-augmented ECG Signals](https://mlsanity.com/paper/2202.00569/) (2022)
- [Leveraging Statistical Shape Priors in GAN-based ECG Synthesis](https://mlsanity.com/paper/2211.02626/) (2022)
- [Visual Evaluation of Generative Adversarial Networks for Time Series Data](https://mlsanity.com/paper/2001.00062/) (2019)
- [ECGAN: Self-supervised generative adversarial network for electrocardiography](https://mlsanity.com/paper/2301.09496/) (2023)
- [Learning disentangled representation from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia](https://mlsanity.com/paper/1808.01524/) (2018)
- [Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors](https://mlsanity.com/paper/1811.00073/) (2018)
- [Evolving SimGANs to Improve Abnormal Electrocardiogram Classification](https://mlsanity.com/paper/2205.10116/) (2022)
- [Out-of-Distribution Detection using BiGAN and MDL](https://mlsanity.com/paper/2206.01851/) (2022)

## Built on by (most cited first, 7 of 7)

- [Entropy-Informed Weighting Channel Normalizing Flow for Deep Generative Models](https://mlsanity.com/paper/2407.04958/) (2024)
- [From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification](https://mlsanity.com/paper/2510.19514/) (2025)
- [SSSD-ECG-nle: New Label Embeddings with Structured State-Space Models for ECG generation](https://mlsanity.com/paper/2407.11108/) (2024)
- [EMGFlow: Robust and Efficient Surface Electromyography Synthesis via Flow Matching](https://mlsanity.com/paper/2604.13685/) (2026)
- [Imputation of Missing Data in Smooth Pursuit Eye Movements Using a Self-Attention-based Deep Learning Approach](https://mlsanity.com/paper/2506.00545/) (2025)
- [Enhancing Fatigue Detection through Heterogeneous Multi-Source Data Integration and Cross-Domain Modality Imputation](https://mlsanity.com/paper/2507.16859/) (2025)
- [Physiological World Models for Human State Transitions](https://mlsanity.com/paper/2608.15309/) (2026)

## Abstract (arXiv)

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.