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Introduction: Sleep staging is an essential component in the diagnosis ofsleep disorders and management of sleep health. It is traditionally measured ina clinical setting and requires a labor-intensive labeling process. Wehypothesize that it is possible to perform robust 4-class sleep staging usingthe raw photoplethysmography (PPG) time series and modern advances in deeplearning (DL). Methods: We used two publicly available sleep databases thatincluded raw PPG recordings, totalling 2,374 patients and 23,055 hours. Wedeveloped SleepPPG-Net, a DL model for 4-class sleep staging from the raw PPGtime series. SleepPPG-Net was trained end-to-end and consists of a residualconvolutional network for automatic feature extraction and a temporalconvolutional network to capture long-range contextual information. Webenchmarked the performance of SleepPPG-Net against models based on thebest-reported state-of-the-art (SOTA) algorithms. Results: When benchmarked ona 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 goodgeneralization performance to an external database, obtaining a $\kappa$ scoreof 0.74 after transfer learning. Perspective: Overall, SleepPPG-Net providesnew SOTA performance. In addition, performance is high enough to open the pathto the development of wearables that meet the requirements for usage inclinical applications such as the diagnosis and monitoring of obstructive sleepapnea.

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