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A Review of Deep Learning Methods for Photoplethysmography Data

Guangkun Nie, Jiabao Zhu, Gongzheng Tang, Deyun Zhang, Shijia Geng, Qinghao Zhao, Shenda Hong · Jan 23, 2024 · Citations: 0

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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices. In recent years, the integration of deep learning has substantially advanced PPG signal analysis and broadened its applications across both healthcare and non-healthcare domains. Methods: We conducted a comprehensive review of studies applying deep learning to PPG data published between January 1, 2017 and December 31, 2025, retrieved from Google Scholar, PubMed, and Dimensions. The included studies were analyzed from three key perspectives: tasks, models, and data. Results: A total of 460 papers were included that applied deep learning techniques to PPG signal analysis. These studies span a wide range of application domains, including traditional physiological monitoring tasks such as cardiovascular assessment, as well as emerging applications such as 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 based on handcrafted features, deep learning methods generally achieve improved performance and provide greater flexibility in model development. Nevertheless, several challenges remain, including the limited availability of large-scale high-quality datasets, insufficient validation in real-world environments, and concerns regarding model interpretability, scalability, and computational efficiency. Addressing these challenges and exploring emerging research directions will be essential for further advancing deep learning-based PPG analysis.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
  • The abstract does not clearly describe the evaluation setup.
  • The abstract does not clearly name benchmarks or metrics.
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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

Background context only.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 15%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: Medicine

Evaluation Details

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices.
  • In recent years, the integration of deep learning has substantially advanced PPG signal analysis and broadened its applications across both healthcare and non-healthcare domains.
  • Methods: We conducted a comprehensive review of studies applying deep learning to PPG data published between January 1, 2017 and December 31, 2025, retrieved from Google Scholar, PubMed, and Dimensions.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • Background: Photoplethysmography (PPG) is a non-invasive optical sensing technique widely used to capture hemodynamic information and is extensively deployed in both clinical monitoring systems and wearable devices.
  • In recent years, the integration of deep learning has substantially advanced PPG signal analysis and broadened its applications across both healthcare and non-healthcare domains.
  • Methods: We conducted a comprehensive review of studies applying deep learning to PPG data published between January 1, 2017 and December 31, 2025, retrieved from Google Scholar, PubMed, and Dimensions.

Why It Matters For Eval

  • Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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