Gender dependant snore sound based multi feature obstructive sleep apnea screening method

de Silva, Shaminda, Abeyratne, Udantha and Hukins, C. (2012). Gender dependant snore sound based multi feature obstructive sleep apnea screening method. In: Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'12). 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'12), San Diego, United States, (6353-6356). 28 August - 1 September 2012. doi:10.1109/EMBC.2012.6347447

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author de Silva, Shaminda
Abeyratne, Udantha
Hukins, C.
Title of paper Gender dependant snore sound based multi feature obstructive sleep apnea screening method
Conference name 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'12)
Conference location San Diego, United States
Conference dates 28 August - 1 September 2012
Proceedings title Proceedings of the 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'12)   Check publisher's open access policy
Journal name IEEE Engineering in Medicine and Biology Society. Conference Proceedings   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2012
Sub-type Fully published paper
DOI 10.1109/EMBC.2012.6347447
ISBN 97814244-41198
9781424441204
ISSN 1557-170X
Start page 6353
End page 6356
Total pages 7
Collection year 2013
Language eng
Abstract/Summary Obstructive Sleep Apnea (OSA) is a serious sleep disorder that occurs due to collapsing upper airways (UA). More than 80% of OSA sufferers remain undiagnosed and the situation demands simplified, convenient technology for community screening. Almost all OSA patients snore and snoring is the earliest nocturnal symptom of OSA. Snore signals (SS) are produced due to vibration of soft tissues in the narrowed parts of the UA. It is known that the UA properties are gender specific. In this paper, we work under the hypothesis that gender specific analysis of snore sounds should lead to a higher OSA detection performance. We propose a snore based multi-parametric OSA screening technique, which incorporates the gender differences in the algorithm. The multi feature vector was modeled using logistic regression based algorithms to classify subjects into OSA/non-OSA classes. The performance of the proposed method was evaluated by carrying out K-fold cross validation. This procedure was applied to male (n=51) and female (n=36) data sets recorded in a clinical sleep laboratory. Each data set consisted of sound recordings of 6–8 hr. duration. The performance of the method was evaluated against the standard laboratory method of diagnosis known as polysomongraphy. Our gender-specific technique resulted in a sensitivity of 93±9% with specificity 89±7% for females and sensitivity of 91±8% with specificity 89±12% for males. These results establish the possibility of developing cheap, convenient, non-contact and an unattended OSA screening technique.
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 1 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Wed, 14 Nov 2012, 22:39:00 EST by Dr Udantha Abeyratne on behalf of School of Information Technol and Elec Engineering