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Model

PG-LRF

From “PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation”
PPGECGNeurIPS 2026Weights: not released
Paper Cite
Venue listedCatalog paper date: 2026Added to atlas: Oct 25, 2026Source

Activity counts measure attention, not model quality. Missing counts are unknown. Publication status follows the linked source; a listed venue alone does not confirm peer review.

PG-LRF generates ECG from PPG with a physiology-guided latent rectified flow. An electro-hemodynamic simulator supplies morphology and forward PPG consistency so the generator respects cardiac phase and pulse-arrival delay while needing only PPG at inference.

Venue listing
NeurIPS 2026 (Carl Yang Emory page)
Signals
PPG, ECG
MC-MED
MAE 0.71, RMSE 1.07, FID 37.79, HR MAE 3.94 bpm
Year
2026

arXiv 2605.12541

Downstream CVD Macro-AUROC 0.633 on generated ECG

No public code or weights listed with the preprint

At a glance
Type
Task model
Modalities
PPGECG
Form factor
Clinical ed monitor
Parameters
Not reported
Architecture
Physiology-Aware AutoEncoder + PPG-conditioned latent rectified flow with electro-hemodynamic simulator guidance
Venue
NeurIPS 2026 (listed on Emory CS page)
Year
2026
arXiv
2605.12541 (opens in a new tab)
DOI
Not reported
Code
Not released
Weights
Not released
Licence
Not reported
Caveats
Not reported
Datasets
Not reported
Papers
Added
Oct 25, 2026
Updated
Oct 25, 2026
PG-LRF: ChatPPG research summary card

How it works

How PG-LRF works: Input signals → Model → Downstream tasksInput signals: PPG (Photoplethysmogram); ECG (Electrocardiogram). Model: PG-LRF.Input signalsPPG: PhotoplethysmogramPPGPhotoplethysmogramECG: ElectrocardiogramECGElectrocardiogramModelPG-LRF
Architecture
Physiology-Aware AutoEncoder + PPG-conditioned latent rectified flow with electro-hemodynamic simulator guidance
Inputs
PPG, ECG
Approach
PPG · ECG · ppg to ecg · generative · rectified flow · physiology guided

Datasets

No public datasets linked yet.

Papers· 2

Cite

APA

Wang, X., Wang, M., Han, K., Cao, D., Chang, C., Shi, Y., Yan, R., Luo, X., Liu, Y., Hu, X., Sun, Y., Wang, W., & Yang, C. (2026). PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation. NeurIPS 2026. https://arxiv.org/abs/2605.12541

BibTeX

@inproceedings{wang2026pglrf,
  title         = {{PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation}},
  author        = {Wang, Xiaoda and Wang, Minxiao and Han, Kaiqiao and Cao, Defu and Chang, Ching and Shi, Yidan and Yan, Runze and Luo, Xiao and Liu, Yan and Hu, Xiao and Sun, Yizhou and Wang, Wei and Yang, Carl},
  year          = {2026},
  booktitle     = {NeurIPS 2026},
  eprint        = {2605.12541},
  archivePrefix = {arXiv}
}

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History

  1. Added to the ChatPPG research hub

Connections

Open in the graph explorer
Signal: PPG (uses signal)PPGPPGSignal: ECG (uses signal)ECGECGPaper: Position: Beyond Prediction: Toward Verifiable Physiological Waveform Reasoning with Foundation Models and Agentic LLMs (paper and model)Position: Beyond Pr…Position:…Paper: PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation (paper and model)PG-LRF: Physiology-…PG-LRF: Ph…PG-LRFPG-LRF
Signals 2
PPG, ECG

Same tasks, data and authors