Deep learning advances PPG signal analysis, but challenges persist

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

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

排序理由 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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