• Preity Patil
• Kalpana Walhe
• Shubhada Chelekar
• Sakshi Saykar
• Deepali Dhadwad

Sleep apnea (SA) is the most common respiratory sleep disorder, leading to some serious neurological and cardiovascular diseases if left untreated. Sleep apnea is an obstructive sleep apnea disorder that affects a large proportion of the population. Our proposed System helps to analyze and detect the Presence of the apnea and hypopnea in the individual using CNN. A convolutional neural network (CNN) is a kind of deep neural network that can automatically learn effective feature representation from training data and has been successfully applied in many fields. In this system we have considered 12 features that are mainly responsible for the sleep apnea. There are some dependent variables or Predicted variable that helps to get the factors that mostly dependent on key variables that mainly includes Apnea-Hypopnea Index (AHI), Central apnea, Oxygen Desaturation and Hypopnea.

Obstructive sleep Apnea, Convolution Neural Network, Dependent Variable Analysis, Feature Understanding, Exploratory Data Analysis, Apnea Hypopnea Index, Central apnea, Oxygen Desaturation and Hypopnea

"Expert System For Automatic Sleep Disorder Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.g369-g372, April-2022, Available :http://www.jetir.org/papers/JETIR2204656.pdf

"Expert System 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 4, page no. ppg369-g372, April-2022, Available at : http://www.jetir.org/papers/JETIR2204656.pdf