A Review of Deep Learning Methods for Photoplethysmography Data

arXiv:2401.12783v2 Announce Type: replace
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.