Clinical Evaluation of PPG-Based Deep Learning Models for Sleep Staging in Patients with Suspected Sleep Apnea

Traditionally used for measuring heart rate and oxygen saturation, photoplethysmography (PPG) has emerged as a promising non-invasive alternative for diagnosing sleep related disorders. Unlike the gold-standard polysomnography (PSG) performed in-lab at the hospital, PPG offers a more scalable and cost-effective solution. Recent advancements in deep learning have significantly enhanced the precision of these methods for sleep stage inference. This study extends the evaluation of a PPG-based deep learning model to a clinical cohort of 134 patients with suspected sleep apnea (SA). These participants, enrolled in an ongoing clinical trial, underwent overnight PSG alongside simultaneous recording of PPG and accelerometer signals using CSEM's wearable devices, positioned at both the wrist and upper arm. When compared to PSG, the PPG-based deep learning model achieved a median accuracy of 80.8% with a Cohen's Kappa of 0.7 in identifying wakefulness, light sleep (S1 + S2), deep sleep (S3), and rapid eye movement (REM) sleep stages using wrist-worn sensors. A reduction in performance was observed when the device was worn at the upper arm, with accuracy decreasing by approximately 6.2% and Cohen's Kappa by 10%. Additionally, a lightweight alternative of the model leveraging inter-beat-intervals (IBIs) yielded comparable results at the wrist, with no performance degradation at the upper arm, highlighting its potential for deployment in resource-constrained settings. Overall, these findings demonstrate the feasibility of the approach as an accessible complement to PSG for home-based sleep monitoring.