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PaperarXiv:2307.06162

Deep Generative Models for Physiological Signals: A Systematic Literature Review

Venue listedDated Jul 2025Added Nov 10, 2026

  • NJ

Authors: Nour Neifar; Afef Mdhaffar; Achraf Ben-Hamadou; Mohamed Jmaiel

Artificial Intelligence in Medicine 165:103127 (2025)

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TL;DR

Systematic review (Neifar, Mdhaffar, Ben-Hamadou, Jmaiel) of deep generative models for ECG, EEG, PPG and EMG: applications, challenges, evaluation protocols used, and most-used physiological databases. Background reference for synthetic-signal / augmentation work, not a new method.

  • Form factor: n/a (review)

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.

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Tasks & signals

Signals

PPGECGEEGEMG

Keywords

ReviewGenerative ModelsSynthetic DataGanVaeDiffusionData Augmentation

How it works

How Method works: Setup → Method → TargetSetup: Signals (ECG, EEG, PPG, EMG); Form factor (n/a (review)). Method: Method.SetupSignals: ECG, EEG, PPG, EMGSignalsECG, EEG, PPG, EMGForm factor: n/a (review)Form factorn/a (review)MethodMethod

Cite this paper

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Neifar, N., Mdhaffar, A., Ben-Hamadou, A., & Jmaiel, M. (2025). Deep Generative Models for Physiological Signals: A Systematic Literature Review. Artificial Intelligence in Medicine, 165, 103127. https://doi.org/10.1016/j.artmed.2025.103127
Neifar, Nour, Afef Mdhaffar, Achraf Ben-Hamadou, and Mohamed Jmaiel. 2025. "Deep Generative Models for Physiological Signals: A Systematic Literature Review." Artificial Intelligence in Medicine 165: 103127. https://doi.org/10.1016/j.artmed.2025.103127.
Neifar, N., Mdhaffar, A., Ben-Hamadou, A. and Jmaiel, M. (2025) 'Deep Generative Models for Physiological Signals: A Systematic Literature Review', Artificial Intelligence in Medicine, 165, p. 103127. doi: 10.1016/j.artmed.2025.103127.
Neifar, Nour, et al. "Deep Generative Models for Physiological Signals: A Systematic Literature Review." Artificial Intelligence in Medicine, vol. 165, 2025, p. 103127. https://doi.org/10.1016/j.artmed.2025.103127.
N. Neifar, A. Mdhaffar, A. Ben-Hamadou, and M. Jmaiel, "Deep Generative Models for Physiological Signals: A Systematic Literature Review," Artificial Intelligence in Medicine, vol. 165, p. 103127, 2025, doi: 10.1016/j.artmed.2025.103127.
@article{neifar2025deep,
  title         = {{Deep Generative Models for Physiological Signals: A Systematic Literature Review}},
  author        = {Neifar, Nour and Mdhaffar, Afef and Ben-Hamadou, Achraf and Jmaiel, Mohamed},
  year          = {2025},
  journal       = {Artificial Intelligence in Medicine},
  volume        = {165},
  pages         = {103127},
  doi           = {10.1016/j.artmed.2025.103127},
  eprint        = {2307.06162},
  archivePrefix = {arXiv}
}

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