A COMPREHENSIVE REVIEW ON SLEEP APNEA DETECTION USING HYBRID MACHINE LEARNING AND DEEP LEARNING MODELS
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Abstract
Sleep apnea is a prevalent and serious sleep disorder characterized by repeated interruptions in breathing during sleep, increasing risks of cardiovascular disease, cognitive impairment, and reduced quality of life. The disorder occurs in three main forms: obstructive, central, and complex sleep apnea, with obstructive sleep apnea (OSA) being the most prevalent. Common risk factors include obesity, aging, smoking, alcohol use, and genetic predisposition. Traditionally, diagnosis relies on polysomnography, which is accurate but costly and time-intensive. To address these limitations, recent studies have applied machine learning (ML) and deep learning (DL) techniques for automated detection and classification of sleep apnea. Hybrid and ensemble models, such as Gradient Boosting and CatBoost, have shown promising results with classification accuracies exceeding 97%. These advancements suggest that AI-driven approaches can offer scalable, cost-effective alternatives to conventional methods, supporting early detection and improved management of sleep apnea in both clinical and home settings.
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