SLEEP 2011 Abstract Supplement

B. Clinical Sleep Science XIV. Instrumentation and Methodology
of cardiorespiratory coupling. We assessed Cortico-CPC in Down’s syndrome,
Pervasive Development Disorder, and 15q deletion syndromes,
free of sleep apnea.
Methods: Instantaneous delta power (from the C4-A1 EEG) and HFC
power (from the polysomnogram ECG) were correlated in 2.1 minute
epochs from polysomnograms performed in the target population, at the
Children’s Hospital, Boston. Age matched control data was available.
We analyzed 19, 13 and 6 children with Down’s syndrome, PDD and
15q deletion syndrome, aged 6.1 ± 3.8, 8.3 ± 2.3 and 6.4 ± 5.7, respectively.
Results: Normative data shows a clear developmental profile of Cortico-CPC
(lowest at birth and peaking at 10-12 years, decreasing to an
adult plateau in the 20’s); all three conditions had a reduction relative to
age-matched subjects. Cortico-CPC was found to be 0.442±0.140 and
0.519±0.128 for healthy children ages 5-6 (n=217) and 10-12 (n=44) respectively,
compared with 0.172±0.309 and 0.185±0.225 in Down’s syndrome
ages 5-6 (n=10) and 8-12 (n=9), 0.279±0.274 and 0.449±0.139 in
children with PDD ages 5-6 (n=6) and 8-12 (n=7), and 0.138±0.243 in
2-14 year old children with 15q deletion syndromes (n=6).
Conclusion: Cortico-CPC may be a biomarker of brain health. Poorly
integrated cortical and subcortical activity may provide clues to brain
development, and the risk of sudden death (in the 15q syndrome).
Support (If Any): NIH Grant 1RC1HL099749-01, NIH-sponsored Research
Resource for Complex Physiologic Signals (UO1EB008577)
0952
RELATIONSHIP BETWEEN THE HIGHER ORDER
STATISTICAL FEATURES OF SNORING SOUNDS AND
ANTHROPOMETRIC FACTORS OF SNORERS
Azarbarzin A 1,2 , Moussavi Z 1,2
1
Electrical and Computer Engineering, University of Manitoba,
Winnipeg, MB, Canada, 2 E-Health, TRLabs, Winnipeg, MB, Canada
Introduction: Snoring sounds have been shown to have non-linear behavior.
Higher order statistical (HOS) techniques can reveal non-linear
properties of snoring sound (SS) segments. While there are few studies
investigating the effect of anthropometric factors on spectral behavior of
snoring sounds, there was no study to investigate the effect of anthropometric
factors such as age, height, weight, BMI on the non-linear properties
of snoring sounds, which is the focus of this study.
Methods: The respiratory sound signals were collected from 30 patients
with different levels of airway obstruction by a microphone placed over
the subjects’ trachea simultaneously with full-night Polysomnography
during sleep. The SS segments were identified automatically from respiratory
sounds using our recent method. HOS features including
skewness, kurtosis, and Mean Peak Frequency (MPF) were estimated
from the 15-minute randomly selected SS segments of all subjects. The
subjects were also divided into different groups based on their height
(G1: 179 cm), weight (G1: 100kg), BMI (G1: 35), and age (G1: 53). The effect of anthropometric factors on HOS features
was investigated using statistical Analysis of Variance (ANOVA).
Results: Kurtosis and skewness showed significant differences (p