Feature extraction for snore sound via neural network processing

Emoto, T., Abeyratne, U. R., Akutagawa, M., Nagashino, H. and Kinouchi, Y. (2007). Feature extraction for snore sound via neural network processing. In: A. Dittmar and J. Clark, Proceedings of the 29th Annual International Conference of the IEEE EMBS. 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2007), Lyon, France, (5477-5480). 23-26 August 2007. doi:10.1109/IEMBS.2007.4353585


Author Emoto, T.
Abeyratne, U. R.
Akutagawa, M.
Nagashino, H.
Kinouchi, Y.
Title of paper Feature extraction for snore sound via neural network processing
Conference name 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2007)
Conference location Lyon, France
Conference dates 23-26 August 2007
Proceedings title Proceedings of the 29th Annual International Conference of the IEEE EMBS   Check publisher's open access policy
Journal name 2007 Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Vols 1-16   Check publisher's open access policy
Place of Publication Piscataway, NJ, U.S.A.
Publisher IEEE - Institute of Electrical Electronics Engineers Inc.
Publication Year 2007
Sub-type Fully published paper
DOI 10.1109/IEMBS.2007.4353585
ISBN 978-1-4244-0787-3
ISSN 1557-170X
Editor A. Dittmar
J. Clark
Start page 5477
End page 5480
Total pages 4
Collection year 2008
Language eng
Abstract/Summary Snore sound (SS) is the earliest and the most common symptom of Obstructive Sleep Apnea (OSA) which is a serious disease caused by the collapse of upper airways during sleep. SS should carry vital information on the state of the upper airways and is simple to acquire and rich in features but their analysis is complicated. In this study we use neural network (NN) based method to model SS via a simple second order one-step predictor. We show that the some hidden information/feature of a SS can be conveniently captured in the connection-weight-space (CWS) of the NN, after a process of supervised training. The availability of the proposed method is investigated by performing independent component analysis (ICA) on CWS.
Subjects E1
291500 Biomedical Engineering
730110 Respiratory system and diseases (incl. asthma)
Keyword Obstructive Sleep Apnea (OSA)
Snore sound
Neural network processing
Independent component analysis (ICA)
Q-Index Code E1
Q-Index Status Confirmed Code

 
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Created: Wed, 07 May 2008, 00:48:11 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering