# SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography

arXiv: https://arxiv.org/abs/2202.05735 · HTML: https://mlsanity.com/paper/2202.05735/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 75 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification](https://mlsanity.com/paper/2105.11043/) (2021)
- [Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions](https://mlsanity.com/paper/2111.08446/) (2021)
- [SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging](https://mlsanity.com/paper/2105.13864/) (2021)
- [Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques](https://mlsanity.com/paper/2005.03357/) (2020)
- [Sleep Staging from Electrocardiography and Respiration with Deep Learning](https://mlsanity.com/paper/1908.11463/) (2019)
- [Digital biomarkers and artificial intelligence for mass diagnosis of atrial fibrillation in a population sample at risk of sleep disordered breathing](https://mlsanity.com/paper/2007.14686/) (2020)

## Built on by (most cited first, 13 of 13)

- [Generalization in medical AI: a perspective on developing scalable models](https://mlsanity.com/paper/2311.05418/) (2023)
- [RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG](https://mlsanity.com/paper/2401.05411/) (2023)
- [wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological Signals](https://mlsanity.com/paper/2411.04644/) (2024)
- [SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers](https://mlsanity.com/paper/2404.03831/) (2024)
- [SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms](https://mlsanity.com/paper/2410.22646/) (2024)
- [On Improving PPG-Based Sleep Staging: A Pilot Study](https://mlsanity.com/paper/2508.02689/) (2025)
- [A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals](https://mlsanity.com/paper/2308.05759/) (2023)
- [Optimizing Photoplethysmography-Based Sleep Staging Models by Leveraging Temporal Context for Wearable Devices Applications](https://mlsanity.com/paper/2410.00693/) (2024)
- [Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography](https://mlsanity.com/paper/2412.15947/) (2024)
- [SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography](https://mlsanity.com/paper/2404.06869/) (2024)
- [uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm](https://mlsanity.com/paper/2506.11238/) (2025)
- [Combining scEEG and PPG for reliable sleep staging](https://mlsanity.com/paper/2602.15042/) (2026)
- [Rethinking PPG-based Sleep Staging: Datasets, Metrics, and Benchmarks](https://mlsanity.com/paper/2608.00943/) (2026)

## Abstract (arXiv)

Introduction: Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. It is traditionally measured in a clinical setting and requires a labor-intensive labeling process. We hypothesize that it is possible to perform robust 4-class sleep staging using the raw photoplethysmography (PPG) time series and modern advances in deep learning (DL). Methods: We used two publicly available sleep databases that included raw PPG recordings, totalling 2,374 patients and 23,055 hours. We developed SleepPPG-Net, a DL model for 4-class sleep staging from the raw PPG time series. SleepPPG-Net was trained end-to-end and consists of a residual convolutional network for automatic feature extraction and a temporal convolutional network to capture long-range contextual information. We benchmarked the performance of SleepPPG-Net against models based on the best-reported state-of-the-art (SOTA) algorithms. Results: When benchmarked on a held-out test set, SleepPPG-Net obtained a median Cohen's Kappa (kappa) score of 0.75 against 0.69 for the best SOTA approach. SleepPPG-Net showed good generalization performance to an external database, obtaining a kappa score of 0.74 after transfer learning. Perspective: Overall, SleepPPG-Net provides new SOTA performance. In addition, performance is high enough to open the path to the development of wearables that meet the requirements for usage in clinical applications such as the diagnosis and monitoring of obstructive sleep apnea.