Electrical and Computer Engineering Department-College of Engineering-Sultan Qaboos University
Statistical Single Characterization and Subband Decomposition for
Heart Rate Variability Analysis In Patients With Obstructive Sleep
Apnea
Bader AL Ghunaimi
Abstract
Obstructive Sleep Apnea (OSA) is the cessation of breathing during sleep due to the collapse of
upper airway. Apnea patients have increased risk of hypertension and heart failures.
Polysomnographic recording is a conventional method for detection of OSA. Although it
provides reliable results, it is expensive and cumbersome. Time domain and frequency domain
analysis of heart rate variability (HRV) are new promising and non-invasive methods for
detection of OSA. Many physiological activities are reflected as cyclic variations in RR interval
(RRI) measures of HRV. Many studies have shown that OSA patients tend to have spectral peaks
between 0.01 and 0.05 Hz of RRI spectrum. ECG records for 55 severe OSA patients and 35
normal subjects are acquired from Physionet website and from Department of Physiology in
Sultan Qaboos University (SQU) Hospital. RRI data are extracted from these ECG records using
QRS detection software provided by Physionet itself. The generated RRI data are then smoothed
and filtered to remove false intervals and to substitute for missed intervals, as well as to screen
the oscillations of OSA. Three types of RRI data groups are generated: non- filtered RRI (RRINF), band pass filtered (RRI-F1) and high pass filtered (RRI-F2). Each RRI data group is
analyzed independently. In this thesis, two new analysis methods are used to screen OSA and
normal subjects. These methods are: Hilbert Transform followed by Statistical Signal
Characterization (HTSSC) method and Sub-band decomposition (SB) method. HTSSC is a time
domain analysis that manipulates RRI data in minute-by-minute basis and extracts 8 statistical
parameters for each minute. The ability of these parameters to screen OSA is assessed by
Receiver Operating Characteristics (ROC). Three of those parameters: mean of amplitude (MA),
deviation of amplitude (DA) and maximum amplitude (MAXA), are found to produce the best
range of accuracy (93-96%), specificity (93-100%), and sensitivity (87-100%) results. SB is a
frequency domain analysis that is used to estimate the power spectral density (PSD) of RRI data
without performing actual transformation to frequency domain, and to define the three bands of
Electrical and Computer Engineering Department-College of Engineering-Sultan Qaboos University
RRI spectrum: very low frequency (VLF), low frequency (LF) and high frequency (HF). The
power ratios LFIHF and LF/VLF are then calculated for each RRI data group. The performance
is also evaluated by ROC. LF/VLF produced the best results when used with non-filtered RRI d