Detection of sleep apnea using polysomnographic signals

Abstract

Introduction: Sleep is important in humans, and it is affected by lifestyle changes. Improper sleep leads to serious physiological problems and disorders that occurs in human brain/scalp. These physiological changes and electrical activity of the human brain are recorded as electroencephalogram (EEG) signals. This paper describes the detection of a major sleep disorder \textit{i.e.}, sleep apnea (SA). Methods: In this paper, sleep apnea is measured using various artifacts taken from the subjects. The discrete wavelet transform (DWT) is used to extract characteristics from an electroencephalogram (EEG) signal and to detect sleep. This is used to determine whether a person has obstructive sleep apnea (OSA) or central sleep apnea (CSA). The wavelet technique is used to split the EEG signal into five frequency bands: delta, theta, alpha, beta, and gamma. Results: For these five frequency bands, the mean, standard deviation, variance, maximum, minimum, and energy are computed. Discussion: A sleep problem is detected based on these characteristics.