# PENGUIN: General Vital Sign Reconstruction from PPG with Flow Matching State Space Model

arXiv: https://arxiv.org/abs/2602.03858 · HTML: https://mlsanity.com/paper/2602.03858/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022)
- [Large-scale Training of Foundation Models for Wearable Biosignals](https://mlsanity.com/paper/2312.05409/) (2023)
- [PaPaGei: Open Foundation Models for Optical Physiological Signals](https://mlsanity.com/paper/2410.20542/) (2024)
- [Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation](https://mlsanity.com/paper/2308.13568/) (2023)
- [WildPPG: A Real-World PPG Dataset of Long Continuous Recordings](https://mlsanity.com/paper/2412.17540/) (2024)
- [An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks](https://mlsanity.com/paper/2105.00594/) (2021)
- [A Scoping Review of Deep Learning Methods for Photoplethysmography Data](https://mlsanity.com/paper/2401.12783/) (2024)
- [RespDiff: An End-to-End Multi-scale RNN Diffusion Model for Respiratory Waveform Estimation from PPG Signals](https://mlsanity.com/paper/2410.04366/) (2024)

## Built on by (most cited first, 2 of 2)

- [Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction](https://mlsanity.com/paper/2609.27473/) (2026)
- [Cylindrical Geodesic Flow Matching for Quasiperiodic Physiological Signal Transformation](https://mlsanity.com/paper/2610.08510/) (2026)

## Abstract (arXiv)

Photoplethysmography (PPG) plays a crucial role in continuous cardiovascular health monitoring as a non-invasive and cost-effective modality. However, PPG signals are susceptible to motion artifacts and noise, making accurate estimation of vital signs such as arterial blood pressure (ABP) challenging. Existing estimation methods are often restricted to a single-task or environment, limiting their generalizability across diverse PPG decoding scenarios. Moreover, recent general-purpose approaches typically rely on predictions over multi-second intervals, discarding the morphological characteristics of vital signs. To address these challenges, we propose PENGUIN, a generative flow-matching framework that extends deep state space models, enabling fine-grained conditioning on PPG for reconstructing multiple vital signs as continuous waveforms. We evaluate PENGUIN using six real-world PPG datasets across three distinct vital sign reconstruction tasks (electrocardiogram reconstruction, respiratory monitoring, and ABP monitoring). Our method consistently outperformed both task-specific and general-purpose baselines, demonstrating PENGUIN as a general framework for robust vital sign reconstruction from PPG.