• Prof. Mahendra Nivangune
• Vrushali Jenekar
• Asmita Kurandale
• Ojasvi Mhatre

The Word Sleep apnea (SA) in the term of Obstructive sleep apnea (OSA) is becoming the most ordinary respiratory disorder during sleep, which is distinguish by stopping of airflow to the lungs. These interrupt in breathing must last for more than 10 seconds to be considered an apnea event. Apnea occurrence may occur 5 to 30 times an hour and may occur up to four hundred times per night in those with severe Sleep Apnea. The most frequent night symptoms of SA can mainly include the activities like snoring, nocturnal arousals, sweating, restless sleep and many more. Moreover, sleeping disorders, symptoms of sleep apnea do not occur just during the night. Daytime symptoms also can range from morning headaches, depression, impaired concentration and excessive sleepiness which cause mortality from traffic and industrial accidents. This survey paper aims to bring the different techniques to identify sleep apnea syndrome by using the different features of an individual, because dependent features have been found most effective and efficient to detect the sleep apnea disorders. In this paper a comparative analysis has been prepared between the different techniques used.

Sleep Apnea, Obstructive sleep Apnea (OSA), Convolution Neural Network (CNN)

"A Review on Machine Learning Based Approaches for Automatic Sleep Disorder Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 3, page no.c28-c32, March-2022, Available :http://www.jetir.org/papers/JETIR2203206.pdf

"A Review on Machine Learning Based Approaches for Automatic Sleep Disorder Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 3, page no. ppc28-c32, March-2022, Available at : http://www.jetir.org/papers/JETIR2203206.pdf