  Background: Sleep staging is a fundamental component in the diagnosis of
sleep disorders and the management of sleep health. Traditionally, this
analysis is conducted in clinical settings and involves a time-consuming
scoring procedure. Recent data-driven algorithms for sleep staging, using the
photoplethysmogram (PPG) time series, have shown high performance on local test
sets but lower performance on external datasets due to data drift. Methods:
This study aimed to develop a generalizable deep learning model for the task of
four class (wake, light, deep, and rapid eye movement (REM)) sleep staging from
raw PPG physiological time-series. Six sleep datasets, totaling 2,574 patients
recordings, were used. In order to create a more generalizable representation,
we developed and evaluated a deep learning model called SleepPPG-Net2, which
employs a multi-source domain training approach.SleepPPG-Net2 was benchmarked
against two state-of-the-art models. Results: SleepPPG-Net2 showed consistently
higher performance over benchmark approaches, with generalization performance
(Cohen's kappa) improving by up to 19%. Performance disparities were observed
in relation to age, sex, and sleep apnea severity. Conclusion: SleepPPG-Net2
sets a new standard for staging sleep from raw PPG time-series.
