  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.
