# SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography Source: [SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:09:48.320Z Authors: Kevin Kotzen, Peter H. Charlton, Sharon Salabi, Lea Amar, Amir Landesberg, Joachim A. Behar Published: 2022-02-11T16:17:42.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Four-Class Cardio-Respiratory Sleep Staging on MESA](): four-class cardio-respiratory sleep staging; MESA; Kappa\_mu: 0.73. ## Original sources and implementation [Original paper]() [PDF]()