VAMP-Diff: VampPrior Latent Diffusion for Photoplethysmography Modeling
Photoplethysmography (PPG) has become a ubiquitous physiological signal; however, current generative models still struggle to preserve realistic waveform morphology and learn a latent structure that captures cardiac and respiratory physiology. PPG generators trained with adversarial losses can produce plausible waveforms, but provide no inference path from a real signal to a latent representation. Variational autoencoders, on the other hand, map the PPG data to latent codes, although their decoders often blur systolic upstrokes and dampen amplitude and spectral details. Diffusion models improve waveform fidelity, but typically lack an inference path for reconstruction and physiological an...
| Authors | Fatemeh Ghasemi Balouei, Nathan Willemsen, Mahesh Banavar et al. |
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| Primary category | eess.SP |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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