Research Collaboration

Stanford Sleep Center PI Dr. Robson Capasso and Dr. Arash Alavi, in collaboration with Samsung, advance wearable sleep apnea detection and night-to-night variability assessment

This Samsung-supported project, led by Stanford Sleep Center PI Dr. Robson Capasso , brought together collaborators from the Stanford Sleep Center and Stanford Genetics, Dr. Arash Alavi that resulted in two high-impact papers advancing wearable-based sleep apnea detection and physiologically informed assessment of obstructive sleep apnea.

Wearable Sleep Apnea Detection

The first paper, published in JCSM, evaluates the Samsung Galaxy Watch Sleep Apnea Feature for detecting moderate-to-severe and high-risk obstructive sleep apnea using in-lab PSG as the gold-standard reference.

In this prospective study, 147 participants wore the Galaxy Watch during two in-lab PSG nights, with additional at-home watch-only nights between the lab visits. The study showed strong performance for identifying moderate-to-severe sleep apnea, including an AUROC of 0.94 when compared with PSG-derived obstructive AHI 4%, and excellent performance for detecting OSA within the hypoxic-burden-defined high-risk OSA group.

These findings highlight the potential of consumer wearables to support scalable sleep apnea screening and risk stratification.

Read the JCSM paper →

Night-to-Night Variability in Sleep Apnea Metrics

The second paper, published in CHEST, examines night-to-night variability in 20 polysomnography-derived sleep metrics across two in-lab sleep studies.

The study found that night-to-night variability differs across physiological domains: positional, autonomic, and sleep architecture metrics showed the greatest variability; respiratory event-frequency metrics showed intermediate variability; and oxygenation metrics, including hypoxic burden, were among the most stable.

The study also found that AHI 4% resulted in greater threshold-based diagnostic classification disagreement than AHI 3%/arousal, supporting the need for more calibrated and multi-metric approaches to sleep apnea severity assessment.

Read the CHEST paper →

Looking Ahead

Together, these two studies advance the development and validation of scalable, wearable-enabled approaches for sleep apnea screening, risk stratification, and physiological characterization, with potential implications for improving access to earlier detection and more personalized assessment of obstructive sleep apnea.