Refining Sleep‐Disordered Breathing Annotations Across Multiple Public Sleep Study Datasets

Polysomnography annotations in Sleep Heart Health Study (SHHS), Osteoporotic Fractures in Men Study (MrOS), and Multi-Ethnic Study of Atherosclerosis (MESA) scored apneas and hypopneas solely by flow reduction rather than following the American Academy of Sleep Medicine's (AASM) comprehensive criteria. To address this, we developed a standardized annotation pipeline that integrates sleep staging, oxygen desaturation, and arousal events in accordance with AASM guidelines. This retrospective study analyzed polysomnography data from SHHS1 (n = 5793), SHHS2 (n = 2651), MrOS1 (n = 2907), MrOS2 (n = 1026), MESA (n = 2054), and Korea Image-based Sleep Study (KISS) (n = 7745). We compared reported apnea-hypopnea indices (AHIs) with those derived from original annotations and recalculated values adjusted for sleep stage, desaturation, and arousal. The impact of precise annotation was demonstrated by training two deep learning models, one with original and the other with refined annotations, and comparing their performance in classifying obstructive sleep apnea (OSA) severity. AHIs from original annotations consistently overestimated reported values in SHHS, MrOS, and MESA, with mean absolute errors (MAEs) ranging from 10.3 to 23.6 events/h. After refining the annotations, MAEs were reduced significantly to 0.56-1.29 events/h. KISS, adhering to contemporary scoring guidelines, exhibited high baseline accuracy with an MAE of 0.6 events/h and required no additional refinement. With refined annotations, OSA severity classification F1 score rose from 0.5 to 0.69. Our standardized approach improves cross-cohort consistency, supports both clinical research and AI-based analysis, and enables more reliable use of existing sleep datasets in accordance with current clinical guidelines.