Screening of obstructive sleep apnea based on statistical signal characterization of Hilbert transform of RRI data.
A new technique of time-domain analysis for screening of Obstructive Sleep Apnea (OSA) using R-R interval (RRI) data is investigated. This method is based on the Statistical Signal Characterization (SSC) of the analytical signal that is generated using Hilbert transformation of the RRI data. The four SSC parameters: amplitude mean, period mean, amplitude deviation and period deviation, and their maximum and minimum values are found over a 5-minutes sliding window for both the instantaneous amplitudes and the instantaneous frequencies derived from the analytical signal of the RRI data. Data used in this work are drawn from both MIT database as well as from the Sleep Laboratory at Sultan Qaboos University (SQU) hospital. Threshold values used in the identification of OSA from normal subjects are selected using the Receiver Operating Characteristics (ROC) curves. The new technique classifies correctly 29/30 of MIT Trial data, 27/30 of MIT challenge data, and 30/30 of SQU data.
Al Ghunaimi
Bader
B
Electrical and Computer Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khod, Oman.
Hossen
Abdulnasir
A
Hassan
Mohammed O
MO
D000465:Algorithms; D004562:Electrocardiography; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D015233:Models, Statistical; D017286:Polysomnography; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology; D012984:Software