Exploring the statistical and computational analysis of sleep stages across different age groups

Abstract

Sleep is a crucial part of a healthy life and good sleep may depend on various factors such as sleep duration, sleep efficiency, sleep architecture, sleep latency, sleep fragmentation, etc. Poor sleep quality may lead to the cause of many diseases and disorders. The present work is based on the study and analysis of the polysomnography (PSG) datasets, collected from 82 subjects including 45 females and 37 males. The present work measures sleep stages including Rapid Eye Movement (REM or R), wakefulness (W), Stage-1, Stage-2, and Stage-3/4 of the subjects with age groups of 20–39, 40–59, 60–79, and 80–100 years. This research investigates the average sleeping time percentage in each age group and focuses on the changes in sleep patterns. Furthermore, this investigation employs statistical measures including median, variance, and standard deviation to comprehensively understand the variability of sleep quality and sleep parameters within each age group. The T-tests and ANOVA tests within specific sleep stages for each age group have been measured to determine the significance of age-related variations in sleep parameters. The results appear valid regardless of age and may provide valuable information about the impact on sleep quality. Also, the algorithm has been implemented in a multi-core computing platform with a parallel processing approach and reduced the 96% computation time. The analysis of the present work provides essential information regarding sleep in different age groups, potentially useful for maintaining sleep quality with age.