Statistical Signal Characterization of Spectral Analysis of Heart Rate Variability for Screening of Patients with Obstructive Sleep Apnea

A. Hossen, B. Al Ghunaimi, M.O. Hassan

Abstract


A new screening technique for obstructive sleep apnea (OSA) is implemented. This technique is based on finding the statistical signal characterization (SSC) parameters of the spectrum of pre-processed R-R-interval (RRI) data. A single classification factor (CR) is selected to classify between patients with obstructive sleep apnea and normal controls. Both nonparametric spectral analysis techniques (with Welch method as an example) and parametric techniques (with Burg method as an example) are used to estimate the power spectral density. The data tested in this work are drawn from MIT database. Both trial and test (challenge) groups of data are used. Each of these two groups contains 20 OSA and 10 normal records. The trial data is used to set the threshold value of the classification factor, which is then used in identifying the challenge data. The total accuracy of the screening is about 93% and 90% using Burg and Welch algorithms respectively.

 


Keywords


Sleep apnea, Statistical signal characterization, Parametric and non-parametric spectral analysis

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DOI: http://dx.doi.org/10.24200/tjer.vol3iss1pp1-9

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