Development of a Multimodal Deep Learning Model for Pediatric Patients

This study aims to develop a multimodal data-driven model integrating multiple noninvasive physiological signals to assess the severity of pediatric sleep-disordered breathing, using standard clinical sleep study results as the reference.

Inclusion Criteria: * Individuals with clinical suspicion of obstructive sleep apnea who are referred for polysomnography Exclusion Criteria: * Intolerance to a fingertip or wrap-around pulse oximeter * Presence of significant structural abnormalities of the upper airway * Cardiac arrhythmia * Neuromuscular disease * Hospitalization within the previous one month