• Anita Kaule
• Pooja Lande
• Shraddha Padwal

Sleep apnea is a sleep related or breathing disorder in which breathing stops at the time of sleeping. It causes breathing to repeatedly stop and start during sleep which over years increases the risk of hypertension, heart disease, stroke, Alzheimer’s, and cancer. Detecting such disorder can significantly saves peoples life to a great extent. This paper presents a system that can effectively detects the presence of the apnea and hypopnea in the individual using CNN. The experimentation is carried out on the National Sleep Research Resource dataset. The system builds a prediction model by using Convolution Neural Network (CNN) technique. System can identifies 12 important features that mainly includes Apnea-Hypopnea Index (AHI), Central apnea, Oxygen Desaturation and Hypopnea for the prediction of sleep apnea.

Prediction of sleep apnea, Convolution Neural Network (CNN), Apnea-Hypopnea Index (AHI), Dependent Variable Analysis, Feature extraction.

"Analysing and Predicting Sleep Apnea using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.a232-a239, June-2021, Available :http://www.jetir.org/papers/JETIR2106033.pdf

"Analysing 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 6, page no. ppa232-a239, June-2021, Available at : http://www.jetir.org/papers/JETIR2106033.pdf