Analysis of Macrostructure of Sleep Apnea with Respect to age and Gender Factors on CAP Database

Abstract

The word “apnea”? means “without breath.”? Obstructive Sleep Apnea (OSA) is the most common form of sleep apnea. If OSA left untreated, it can cause high blood pressure and other cardiovascular disease, weight gain, memory problems, impotency and headaches. There are more than 40 million American who suffer from OSA and sadly 38000 cardiovascular deaths are cause by sleep apnea every year. In this study, we investigate the possibility of identifying any form of sleep apnea based on statistical analysis of polysomnography data for seven types of sleep disorder. The analysis explores the probability that gender, age or combination of these factors provide any conclusive evidence of an existing pattern that can be utilized for effective diagnosis. This novel approach of distinguishing sleep apnea in patients based on association of polysomnography data with their age and gender conducted in 4 different broad categories. The functionality of Macro-indices has been examined in order to find the best way to distinguish non-pathologic to pathologic groups. Gender and age are categorized into different groups so that we could test whether data analysis of macro index for any subcategory indicate significant difference from the control sample. The statistical analysis of macro indices for two gender and 6 age subcategories demonstrates that significant statistical differences exist for some indices in a particular subcategory.