Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome

Karunajeewa, AS, Abeyratne, UR and Hukins, C (2011) Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome. Physiological Measurement, 32 1: 83-97. doi:10.1088/0967-3334/32/1/006


Author Karunajeewa, AS
Abeyratne, UR
Hukins, C
Title Multi-feature snore sound analysis in obstructive sleep apnea-hypopnea syndrome
Journal name Physiological Measurement   Check publisher's open access policy
ISSN 0967-3334
1361-6579
Publication date 2011-01-01
Sub-type Article (original research)
DOI 10.1088/0967-3334/32/1/006
Open Access Status DOI
Volume 32
Issue 1
Start page 83
End page 97
Total pages 15
Place of publication Bristol, United Kingdom
Publisher Institute of Physics Publishing
Language eng
Abstract Snoring is the most common symptom of obstructive sleep apnea hypopnea syndrome (OSAHS), which is a serious disease with high community prevalence. The standard method of OSAHS diagnosis, known as polysomnography (PSG), is expensive and time consuming. There is evidence suggesting that snore-related sounds (SRS) carry sufficient information to diagnose OSAHS. In this paper we present a technique for diagnosing OSAHS based solely on snore sound analysis. The method comprises a logistic regression model fed with snore parameters derived from its features such as the pitch and total airway response (TAR) estimated using a higher order statistics (HOS)-based algorithm. Pitch represents a time domain characteristic of the airway vibrations and the TAR represents the acoustical changes brought about by the collapsing upper airways. The performance of the proposed method was evaluated using the technique of K-fold cross validation, on a clinical database consisting of overnight snoring sounds of 41 subjects. The method achieved 89.3% sensitivity with 92.3% specificity (the area under the ROC curve was 0.96). These results establish the feasibility of developing a snore-based OSAHS community-screening device, which does not require any contact measurements.
Keyword Sleep apnea
Snoring
Population screening
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
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 36 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 41 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Sun, 16 Jan 2011, 10:01:54 EST