SleepPPG-Net2: deep learning generalization for sleep staging from photoplethysmography

<i>Objective</i>. sleep staging is essential for diagnosing sleep disorders and managing sleep health. Traditional methods require time-consuming manual scoring. Recent photoplethysmography (PPG)-based deep learning models perform well on local datasets but struggle with external generalization due to data drift.<i>Approach</i>. this study evaluates multi-source domain training for improving out-of-distribution generalization in four-class sleep staging (wake, light, deep, rapid eye movement) from raw PPG time-series. The trained deep learning model is denoted SleepPPG-Net2. Additionally, we examined the impact of demographic factors, ethnicity, and obstructive sleep apnea (OSA) on performance. SleepPPG-Net2 was benchmarked against two state-of-the-art models.<i>Main results</i>. SleepPPG-Net2 outperformed benchmark models, improving generalization performance (Cohen's kappa) by up to 21%. Performance disparities were observed in relation to age, sex, and OSA severity.<i>Significance</i>. SleepPPG-Net2 enhances PPG-based sleep staging and provides insights into demographic and clinical influences on model performance.