# A Scoping Review of Deep Learning Methods for Photoplethysmography Data arXiv: https://arxiv.org/abs/2401.12783 ยท HTML: https://mlsanity.com/paper/2401.12783/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 16 of its 223 references that are in the map. - First posted on arXiv 23 Jan 2024. ## Builds on (most cited first, 16 of 16) - [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021) - [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023) - [Large-scale Training of Foundation Models for Wearable Biosignals](https://mlsanity.com/paper/2312.05409/) (2023) - [Model Complexity of Deep Learning: A Survey](https://mlsanity.com/paper/2103.05127/) (2021) - [Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods](https://mlsanity.com/paper/1903.11260/) (2019) - [CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG](https://mlsanity.com/paper/2010.00104/) (2020) - [Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey](https://mlsanity.com/paper/2005.10691/) (2020) - [Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View](https://mlsanity.com/paper/2112.02625/) (2021) - [Evaluation of PPG Biometrics for Authentication in different states](https://mlsanity.com/paper/1712.08583/) (2017) - [RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation](https://mlsanity.com/paper/2208.08672/) (2022) - [Log-Spectral Matching GAN: PPG-based Atrial Fibrillation Detection can be Enhanced by GAN-based Data Augmentation with Integration of Spectral Loss](https://mlsanity.com/paper/2108.05272/) (2021) - [A Deep Learning & Fast Wavelet Transform-based Hybrid Approach for Denoising of PPG Signals](https://mlsanity.com/paper/2301.06549/) (2023) - [RespNet: A deep learning model for extraction of respiration from photoplethysmogram](https://mlsanity.com/paper/1902.04236/) (2019) - [When the Differences in Frequency Domain are Compensated: Understanding and Defeating Modulated Replay Attacks on Automatic Speech Recognition](https://mlsanity.com/paper/2009.00718/) (2020) - [Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices](https://mlsanity.com/paper/2305.03308/) (2023) - [Novel Blood Pressure Waveform Reconstruction from Photoplethysmography using Cycle Generative Adversarial Networks](https://mlsanity.com/paper/2201.09976/) (2022) ## Built on by (most cited first, 11 of 11) - [Machine-learning for photoplethysmography analysis: Benchmarking feature, image, and signal-based approaches](https://mlsanity.com/paper/2502.19949/) (2025) - [OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis](https://mlsanity.com/paper/2407.06137/) (2024) - [Estimating Clinical Lab Test Result Trajectories from PPG using Physiological Foundation Model and Patient-Aware State Space Model -- a UNIPHY+ Approach](https://mlsanity.com/paper/2509.16345/) (2025) - [FairTune: A Bias-Aware Fine-Tuning Framework Towards Fair Heart Rate Prediction from PPG](https://mlsanity.com/paper/2509.16491/) (2025) - [PENGUIN: General Vital Sign Reconstruction from PPG with Flow Matching State Space Model](https://mlsanity.com/paper/2602.03858/) (2026) - [Deep Imbalanced Regression to Estimate Vascular Age from PPG Data: a Novel Digital Biomarker for Cardiovascular Health](https://mlsanity.com/paper/2406.14953/) (2024) - [Cuffless Blood Pressure Estimation from Six Wearable Sensor Modalities in Multi-Motion-State Scenarios](https://mlsanity.com/paper/2512.01653/) (2025) - [An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness](https://mlsanity.com/paper/2406.07558/) (2024) - [Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learning](https://mlsanity.com/paper/2602.04266/) (2026) - [Artificial intelligence-enabled single-lead ECG for non-invasive hyperkalemia detection: development, multicenter validation, and proof-of-concept deployment](https://mlsanity.com/paper/2603.14177/) (2026) - [Learning Where to Look: A Shared Relative-Alignment Module for Time-Series Forecasting and PPG-to-Vital-Sign Reconstruction](https://mlsanity.com/paper/2609.27473/) (2026) ## Abstract (arXiv) 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.