PaperarXiv:2609.33501

PulseFlow: PPG Counterfactual Generation Via Latent Transport

Preprint2026Added Sep 29, 2026

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Authors: Hung Manh Pham, Dong Ma, Bin Zhu, Pan Zhou

arXiv preprint (Singapore Management University / University of Cambridge)

First page or project page of “PulseFlow: PPG Counterfactual Generation Via Latent Transport” (opens in a new tab)

Pham, Hung Manh et al., "Pulseflow: PPG Counterfactual Generation Via Latent Transport", arXiv:2609.33501, CC BY 4.0

TL;DR

Source-conditioned counterfactual PPG generation: conditional VAE representation + invertible latent transport (conditional RealNVP) to edit cardiac rhythm (SR↔AF) while retaining source demographics / residual. Trained on MIMIC-III-Ext-PPG (~5,668 patients; 30 s @ 125 Hz); same-patient SR–AF pairs guide transport. Reports strong AF conversion (~93.1%) and distributional realism vs generative baselines; on unseen VitalDB scarce-label AF classification, PulseFlow augmentation lifts AUROC (e.g.

  • Form factor: lab

Abstract

Photoplethysmography (PPG) has become an important modality for continuous cardiovascular monitoring, including atrial fibrillation (AF) detection. However, labeled AF recordings remain limited in many clinical settings, making model adaptation difficult when only limited target data are available. Generative modeling offers a natural way to alleviate this scarcity by synthesizing additional AF signals. Existing approaches, however, mainly generate samples that match the target condition without explicitly modeling how an observed source recording should be transformed, making it difficult to leverage abundant source recordings from a specific population or cohort for targeted augmentation. We introduce PulseFlow, a source-conditioned counterfactual generation framework that combines conditional representation learning with invertible latent transport to edit cardiac rhythm while retaining information from the source. Experiments across two clinical cohorts demonstrate effective rhythm transformation, measurable source correspondence, and improved AF classification under limited labels.

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Tasks & signals

Tasks

AF / rhythm

Signals

Keywords

afibaugmentationcounterfactualPPG-rhythm

How it works

How Method works: Setup → Method → TargetSetup: Signals (PPG); Form factor (lab). Method: Method. Target: AF / rhythm.SetupSignals: PPGSignalsPPGForm factor: labForm factorlabMethodMethodTargetAF / rhythmAF / rhythm

Cite this paper

APA

Pham, H. M., Ma, D., Zhu, B., & Zhou, P. (2026). PulseFlow: PPG Counterfactual Generation Via Latent Transport. arXiv preprint. https://doi.org/10.48550/arXiv.2609.33501

BibTeX

@misc{pham2026pulseflow,
  title         = {{PulseFlow: PPG Counterfactual Generation Via Latent Transport}},
  author        = {Pham, Hung Manh and Ma, Dong and Zhu, Bin and Zhou, Pan},
  year          = {2026},
  howpublished  = {arXiv preprint},
  doi           = {10.48550/arXiv.2609.33501},
  eprint        = {2609.33501},
  archivePrefix = {arXiv}
}

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