DocumentCode :
1851225
Title :
Feature Extraction for Snore Sound via Neural Network Processing
Author :
Emoto, T. ; Abeyratne, U.R. ; Akutagawa, M. ; Nagashino, H. ; Kinouchi, Y.
Author_Institution :
Takamatsu Nat. Coll. of Technol., Takamatsu
fYear :
2007
fDate :
22-26 Aug. 2007
Firstpage :
5477
Lastpage :
5480
Abstract :
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.
Keywords :
bioacoustics; diseases; feature extraction; independent component analysis; medical signal processing; neural nets; pneumodynamics; sleep; connection-weight-space; feature extraction; independent component analysis; neural network processing; obstructive sleep apnea; second order one-step predictor method; snore sound; Artificial neural networks; Availability; Diseases; Feature extraction; Independent component analysis; Information analysis; Neural networks; Neurons; Predictive models; Sleep apnea; Artificial Intelligence; Auscultation; Diagnosis, Computer-Assisted; Humans; Neural Networks (Computer); Pattern Recognition, Automated; Reproducibility of Results; Respiratory Sounds; Sensitivity and Specificity; Snoring; Sound Spectrography;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
ISSN :
1557-170X
Print_ISBN :
978-1-4244-0787-3
Type :
conf
DOI :
10.1109/IEMBS.2007.4353585
Filename :
4353585
Link To Document :
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