An investigation into the detection of paediatric obstructive sleep apnoea using polysomnography and machine learning
Abstract
Obstructive sleep apnoea (OSA) in children is frequently characterised by intermittent airway obstruction during sleep, a disease that can cause irregular sleep patterns and possibly long-term health issues. The gold standard for diagnosing OSA in children is polysomnography (PSG), which offers a thorough evaluation of respiratory episodes, sleep stages, and associated physiological markers. Machine learning (ML) approaches have demonstrated promise in recent years for increasing clinical efficiency, automating PSG signal interpretation, and boosting diagnostic precision. In order to diagnose paediatric sleep apnea, this study investigates the integration of PSG data with machine learning techniques.
Fields of Study & Subjects
Obstructive Sleep Apnea Research
Non-Invasive Vital Sign Monitoring
Sleep and related disorders
Publication details
- Journal / source
- International Journal of Artificial Intelligence in Healthcare
- Volume
- 1
- Issue
- 2
- Pages
- 129–135
- Publication date
- 1 January 2026
- Language
- EN