Detection and Classification of Sleep Apnea: Smart Health Solutions

A common sleep ailment that affects millions of people worldwide is sleep apnea. It is typified by frequent shallow or paused breathing while you're asleep, which can cause sleep disorders and can have detrimental effects on one’s health. The condition may significantly affect a person's quality of life, general health, and ability to carry out daily tasks. The quality of life for those with sleep apnea can be considerably improved by early identification of the illness and prompt intervention and therapy. Early treatment of sleep apnea can improve a person's quality of sleep, reduce daytime sleepiness, improve cognitive performance, and increase general wellbeing. The laboratory-based polysomnography (PSG), conventional sleep study diagnostic method is time consuming. Since the development of computer-aided diagnosis (CAD), studying sleep disorders have become increasingly interested in automatic detection since it affects both therapeutic and diagnostic choices. The study literature on sleep apnea that has been published over few years back has been reviewed, with a focus on the many screening techniques used to detect the condition and the software development expertise provided by numerous contributors. This study offers a thorough analysis of hybrid, machine learning, deep learning (DL) methods. Additionally, the study assesses several datasets that have been used in numerous investigations. This study is helpful in shedding light on how various machine learning and deep learning algorithms do in terms of detecting sleep apnea.