A method to screen obstructive sleep apnea using multi-variable non-intrusive measurements

De Silva, S., Abeyratne, U. R. and Hukins, C. (2011) A method to screen obstructive sleep apnea using multi-variable non-intrusive measurements. Physiological Measurement, 32 4: 445-465. doi:10.1088/0967-3334/32/4/006

Author De Silva, S.
Abeyratne, U. R.
Hukins, C.
Title A method to screen obstructive sleep apnea using multi-variable non-intrusive measurements
Journal name Physiological Measurement   Check publisher's open access policy
ISSN 0967-3334
Publication date 2011-04
Sub-type Article (original research)
DOI 10.1088/0967-3334/32/4/006
Volume 32
Issue 4
Start page 445
End page 465
Total pages 211
Place of publication United Kingdom
Publisher Institute of Physics Publishing Ltd.
Collection year 2012
Language eng
Formatted abstract
Obstructive sleep apnea (OSA) is a serious sleep disorder. The current standard OSA diagnosis method is polysomnography (PSG) testing. PSG requires an overnight hospital stay while physically connected to 10-15 channels of measurement. PSG is expensive, inconvenient and requires the extensive involvement of a sleep technologist. As such, it is not suitable for community screening. OSA is a widespread disease and more than 80% of sufferers remain undiagnosed. Simplified, unattended and cheap OSA screening methods are urgently needed. Snoring is commonly associated with OSA but is not fully utilized in clinical diagnosis. Snoring contains pseudo-periodic packets of energy that produce characteristic vibrating sounds familiar to humans. In this paper, we propose a multi-feature vector that represents pitch information, formant information, a measure of periodic structure existence in snore episodes and the neck circumference of the subject to characterize OSA condition. Snore features were estimated from snore signals recorded in a sleep laboratory. The multi-feature vector was applied to a neural network for OSA/non-OSA classification and K-fold cross-validated using a random sub-sampling technique. We also propose a simple method to remove a specific class of background interference. Our method resulted in a sensitivity of 91 ± 6% and a specificity of 89 ± 5% for test data for AHITHRESHOLD = 15 for a database consisting of 51 subjects. This method has the potential as a non-intrusive, unattended technique to screen OSA using snore sound as the primary signal.
Keyword Background sounds
Obstructive sleep apnea
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 27 Oct 2011, 20:42:38 EST by Ms Kimberley Nunes on behalf of Centre for Medical Diagnostic Technologies in Qld