Towards long-term sleep staging via wearable reflective photoplethysmography

STUDY OBJECTIVES: Sleep staging is usually performed by manual scoring of polysomnography (PSG), which is expensive, laborious, and poorly scalable. We propose an alternative to PSG for ambulatory sleep staging using wearable photoplethysmography (PPG) recorded by a smartwatch and automated scoring. METHODS: We previously trained a deep learning model on public datasets, with the specific purpose of performance generalizability to unseen datasets. In the present work, the model was assessed on two datasets of reflective PPG collected from wrist-worn devices: (1) 68 overnight recordings and (2) for the first time, 493 long-term recordings each lasting for 24 hours (170 subjects). Findings were compared either to (1) expert scored sleep stages from PSG for the night recordings or (2) actigraphy for the long-term recordings. RESULTS: For the overnight recordings, the PPG-based model achieved 78.7% accuracy and a Cohen's κ of 0.68 on reflective PPG collected using wrist-worn devices compared to PSG using a 4-class setup (wake, N1, and N2 combined, N3 and REM), and a sleep/wake accuracy of 94.1%, with a Cohen's κ of 0.71. For the long-term recordings, a sleep/wake accuracy of 92.5% with a Cohen's κ of 0.80 was achieved when compared to a state-of-the-art actigraphy-based deep learning model. CONCLUSIONS: This state-of-the-art accuracy achieved on wrist-worn devices represents a significant advancement for home sleep monitoring and a valuable alternative to PSG-based sleep staging. Additionally, our model demonstrated promising results on long-term ambulatory recordings, paving the way towards continuous ambulatory monitoring of sleep stages and sleep-wake cycles. Statement of Significance Sleep staging is crucial to diagnose sleep disorders, but traditional methods are laborious and costly. We developed a sleep staging model that demonstrates high performance and exceptional generalization to unseen datasets, including those from wrist-worn devices, thereby possibly enabling accurate sleep staging from wearable technology. Furthermore, we evaluated the model's performance on 24-hour recordings of subjects of various health conditions, offering valuable insights for clinical applications and future research. These advancements significantly enhance the feasibility of continuous sleep monitoring at home, a low-cost, scalable, and comfortable alternative to current methods.