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Deep learning advances PPG signal analysis, but challenges persist

By PulseAugur Editorial · [1 sources] ·

A scoping review of 460 papers published between 2017 and 2025 reveals that deep learning has significantly advanced the analysis of photoplethysmography (PPG) data. These methods offer improved performance and flexibility over traditional machine learning for tasks ranging from cardiovascular assessment to biometric identification. However, challenges persist regarding dataset availability, real-world validation, model interpretability, scalability, and computational efficiency. AI

IMPACT Highlights the advancements and remaining challenges in applying deep learning to physiological data analysis.

RANK_REASON This is a research paper detailing a scoping review of existing studies. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning advances PPG signal analysis, but challenges persist

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This is a research paper detailing a scoping review of existing studies. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Guangkun Nie, Jiabao Zhu, Gongzheng Tang, Deyun Zhang, Shijia Geng, Qinghao Zhao, Shenda Hong ·

    A Scoping Review of Deep Learning Methods for Photoplethysmography Data

    arXiv:2401.12783v3 Announce Type: replace-cross 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 y…

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