• Archana Gaikwad
• Shivani Dhotre
• Pratibha Ghatole
• Rekha Kale

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. In this paper an attempt is made to develop a system to detect sleep apnea in an individual using National Sleep Research Resource dataset. The system builds a prediction model by using Convolution Neural Network (CNN) technique.

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

"Analyzing and Predicting Sleep Apnea using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.f602-f609, May-2021, Available :http://www.jetir.org/papers/JETIR2105745.pdf

"Analyzing and Predicting Sleep Apnea using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppf602-f609, May-2021, Available at : http://www.jetir.org/papers/JETIR2105745.pdf