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A Scoping Review of Deep Learning Methods for Photoplethysmography Data

T0 review · 2 major / 1 minor · reviewed 2026-05-24 · grok-4.3

Challenge this review Download PDF Read on arXiv ↗

Pith's one-line read Deep learning enables more effective extraction of physiological information from photoplethysmography signals than traditional machine learning.

desk verdict This scoping review tallies 460 DL papers on PPG and sorts them by tasks, models, and data, but its claim that DL generally outperforms traditional ML rests on categorization alone. read the letter →

arxiv 2401.12783 v3 submitted 2024-01-23 cs.AI cs.LGeess.SP

Guangkun Nie , Jiabao Zhu , Gongzheng Tang , Deyun Zhang , Shijia Geng , Qinghao Zhao , Shenda Hong This is my paper · ORCID
classification cs.AIcs.LGeess.SP
keywords photoplethysmographydeeplearningscopingreviewphysiologicalmonitoringwearabledevicessignalprocessingcardiovascularassessment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This scoping review examines 460 papers on deep learning applied to photoplethysmography data published between 2017 and 2025. It analyzes the studies across application tasks, model architectures, and data characteristics. The central finding is that deep learning provides improved performance and flexibility in PPG analysis compared to methods using handcrafted features. The review also identifies ongoing challenges in dataset availability, real-world validation, and model interpretability.

What carries the argument

Scoping review of 460 studies analyzed from the perspectives of tasks, models, and data.

What would settle it

Identification of a large number of additional studies applying deep learning to PPG data from the specified period that were not included in the review.

Watch

Extended reading notes

Core claim

Deep learning has significantly advanced PPG signal analysis by enabling more effective extraction of physiological information. Compared with traditional machine learning approaches reliant on handcrafted features, deep learning methods generally achieve improved performance and offer greater flexibility in model development.

Load-bearing premise

The literature search using Google Scholar, PubMed, and Dimensions for studies from January 1, 2017 to December 31, 2025 captured all relevant papers on deep learning for PPG data.

Editorial extensions

If this is right

  • Deep learning supports traditional tasks like cardiovascular assessment as well as emerging ones such as sleep analysis and biometric identification.
  • Challenges including limited large-scale datasets, insufficient real-world validation, and concerns over interpretability must be addressed for further progress.
  • Integration of deep learning expands PPG applications in both clinical monitoring and wearable devices.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Future work could focus on creating standardized benchmarks for comparing deep learning models on PPG data.
  • Addressing computational efficiency could enable wider deployment in resource-constrained wearable devices.
  • Improved interpretability might increase trust and adoption in clinical settings.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. This scoping review searched Google Scholar, PubMed, and Dimensions for studies applying deep learning to PPG data from 2017–2025, ultimately including 460 papers. The included studies are categorized and mapped from three perspectives (tasks, models, and data), covering domains from cardiovascular monitoring to sleep analysis, cross-modality reconstruction, and biometrics. The conclusions state that deep learning has substantially advanced PPG analysis and generally achieves improved performance and greater flexibility relative to traditional machine-learning methods that rely on handcrafted features, while listing remaining challenges around datasets, real-world validation, interpretability, and efficiency.

Significance. A well-executed scoping review that accurately maps 460 papers could serve as a useful field overview for PPG researchers. However, because the analysis is limited to descriptive categorization without performance metrics, aggregated comparisons, or quantitative synthesis, the significance of the performance-superiority claim is low. The manuscript contains no machine-checked proofs, reproducible code, or falsifiable predictions.

major comments (2)
  1. [Conclusions] Conclusions: The statement that 'deep learning methods generally achieve improved performance' over traditional ML is unsupported by the reported methods and results. The review explicitly restricts analysis to the three perspectives of tasks, models, and data and provides no aggregated performance metrics, counts of studies showing superiority, or direct baseline comparisons; the performance claim therefore rests on an inference the scoping design does not justify.
  2. [Methods] Methods (and Abstract): The literature-search description supplies only high-level database names and date bounds but omits the actual search strings, precise inclusion/exclusion criteria, and any quality-assessment protocol. This makes the reported total of 460 papers difficult to verify or replicate and weakens the central synthesis claim.
minor comments (1)
  1. [Abstract] Abstract: The search window ends on 31 December 2025, after the arXiv posting date of the manuscript; this date range should be explained or corrected.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our scoping review. We address each major comment below and will revise the manuscript accordingly to improve clarity and replicability.

read point-by-point responses

  1. Referee: [Conclusions] Conclusions: The statement that 'deep learning methods generally achieve improved performance' over traditional ML is unsupported by the reported methods and results. The review explicitly restricts analysis to the three perspectives of tasks, models, and data and provides no aggregated performance metrics, counts of studies showing superiority, or direct baseline comparisons; the performance claim therefore rests on an inference the scoping design does not justify.

    Authors: We agree that the performance-superiority claim in the conclusions is not supported by quantitative synthesis or aggregated metrics, as the review is limited to descriptive categorization. We will revise the conclusions section to remove this claim and instead focus on the observed expansion of applications and model flexibility without asserting general performance improvements. revision: yes

  2. Referee: [Methods] Methods (and Abstract): The literature-search description supplies only high-level database names and date bounds but omits the actual search strings, precise inclusion/exclusion criteria, and any quality-assessment protocol. This makes the reported total of 460 papers difficult to verify or replicate and weakens the central synthesis claim.

    Authors: We acknowledge the need for greater methodological transparency. In the revised manuscript we will add the precise search strings employed in Google Scholar, PubMed, and Dimensions, the full inclusion and exclusion criteria applied during screening, and an explicit statement that no formal quality assessment was performed (consistent with scoping-review methodology). revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

Scoping review reports external literature counts with no internal derivations or fitted predictions.

full rationale

The paper is a scoping review that searches external databases, includes 460 papers, and categorizes them by tasks/models/data. No equations, parameter fitting, predictions, or self-citations appear in the provided text. The conclusions synthesize trends from reviewed studies rather than deriving results from the paper's own inputs by construction. The performance claim is an interpretive summary of external work, not a reduction to any fitted quantity or self-referential step within this manuscript.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

This is a literature review paper with no mathematical derivations, fitted parameters, or new postulated entities.

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0 comments
Cite this review

Pith. "Pith review of A Scoping Review of Deep Learning Methods for Photoplethysmography Data." pith.science (2026). https://pith.science/paper/2401.12783

@misc{pith2026240112783,
  author       = {Pith},
  title        = {Pith review of: A Scoping Review of Deep Learning Methods for Photoplethysmography Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2401.12783}},
  note         = {Machine review of arXiv:2401.12783}
}
read the original abstract

Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information, with broad deployment in both clinical monitoring systems and wearable devices. In recent years, the integration of deep learning has substantially advanced PPG signal analysis and expanded its applications across healthcare and non-healthcare domains. Methods: We conducted a comprehensive literature search for studies applying deep learning to PPG data published between January 1, 2017 and December 31, 2025, using Google Scholar, PubMed, and Dimensions. The included studies were analyzed from three key perspectives: tasks, models, and data. Results: A total of 460 papers applying deep learning techniques to PPG signal analysis were included. These studies span a wide range of application domains, from traditional physiological monitoring tasks such as cardiovascular assessment to emerging applications including sleep analysis, cross-modality signal reconstruction, and biometric identification. Conclusions: Deep learning has significantly advanced PPG signal analysis by enabling more effective extraction of physiological information. Compared with traditional machine learning approaches reliant on handcrafted features, deep learning methods generally achieve improved performance and offer greater flexibility in model development. Nevertheless, several challenges remain, including limited availability of large-scale high-quality datasets, insufficient validation in real-world environments, and concerns over model interpretability, scalability, and computational efficiency. Addressing these challenges and exploring emerging research directions will be essential for further progress in deep learning-based PPG analysis.

Figures

Figures reproduced from arXiv: 2401.12783 by the authors.

Figure 1
Figure 1. Overview of analysis based on the aspects of tasks, models, and data [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Framework for literature searching and selection. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AnyPPG: An ECG-Guided PPG Foundation Model Trained on Over 100,000 Hours of Recordings for Holistic Health Profiling

    eess.SP 2025-11 conditional novelty 7.0 of 10

    An ECG-guided PPG foundation model pretrained on 109,909 hours of synchronized PPG-ECG improves standard PPG tasks and reports AUC ≥ 0.7 for 137 ICD-10 disease codes (82 after excluding non-specific labels) in one eme...

  2. MD-ViSCo: A Unified Model for Multi-Directional Vital Sign Waveform Conversion

    cs.SD 2025-06 conditional novelty 6.0 of 10

    A unified model with AdaIN style injection and a Swin/U-Net backbone converts ECG, PPG, and ABP waveforms in all six directions, beating single-direction baselines on public data.

  3. Beyond Single-Channel: Multichannel Signal Imaging for PPG-to-ECG Reconstruction with Vision Transformers

    eess.IV 2025-05 conditional novelty 5.0 of 10

    Representing PPG as a four-channel 2D beat-aligned image and processing it with a Vision Transformer reduces ECG reconstruction error by up to 29% in PRD and 15% in RMSE compared with a 1D CNN baseline.

  4. Pixel Watch: Robust Heart Rate Sensing from Multipath PPG and On-Device Deep Learning Trained on 10,000 hours of Free-Living and Fitness Data

    cs.HC 2026-06 unverdicted novelty 4.0 of 10

    Pixel Watch 2 delivers 1 Hz heart rate from 10 PPG channels via a ~300K-parameter dilated CNN trained on 10k hours, achieving 95% LoA of -10.34 to 8.66 BPM in exercise and -6.57 to 7.48 BPM in free-living validation.

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Pith tools

Reviewed May 24, 2026 · model on record in the stance chip above.