AI Medical Compendium Journal:
Sleep health

Showing 1 to 3 of 3 articles

Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting.

Sleep health
GOAL AND AIMS: One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a m...

Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea.

Sleep health
GOAL AND AIMS: Our objective was to evaluate the performance of Belun Ring with second-generation deep learning algorithms in obstructive sleep apnea (OSA) detection, OSA severity categorization, and sleep stage classification.