Feature Extraction for Snore Sound via Neural Network Processing

• Published in: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2007; pp. 5477 - 5480
• Main Authors: Emoto, T., Abeyratne, U.R., Akutagawa, M., Nagashino, H., Kinouchi, Y.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2007
• Subjects: Artificial Intelligence; Artificial neural networks; Auscultation - methods; Availability; Diagnosis, Computer-Assisted - methods; Diseases; Feature extraction; Humans; Independent component analysis; Information analysis; Neural networks; Neural Networks (Computer); Neurons; Pattern Recognition, Automated - methods; Predictive models; Reproducibility of Results; Respiratory Sounds; Sensitivity and Specificity; Sleep apnea; Snoring - diagnosis; Sound Spectrography - methods
• ISBN: 9781424407873; 1424407877
• ISSN: 1094-687X; 1557-170X; 1558-4615
• DOI: 10.1109/IEMBS.2007.4353585

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.