NDLI: Automatic detection, segmentation and classification of snore related signals from overnight audio recording

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Automatic detection, segmentation and classification of snore related signals from overnight audio recording

Content Provider	IET Digital Library
Author	Qian, Kun Xu, Zhiyong Xu, Huijie Wu, Yaqi Zhao, Zhao
Abstract	Snore related signals (SRS) have been found to carry important information about the snore source and obstruction site in the upper airway of an Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS) patient. An overnight audio recording of an individual subject is the preliminary and essential material for further study and diagnosis. Automatic detection, segmentation and classification of SRS from overnight audio recordings are significant in establishing a personal health database and in researching the area on a large scale. In this study, the authors focused on how to implement this intelligent method by combining acoustic signal processing with machine learning techniques. The authors proposed a systematic solution includes SRS events detection, classifier training, automatic segmentation and classification. An overnight audio recording of a severe OSAHS patient is taken as an example to demonstrate the feasibility of their method. Both the experimental data testing and subjective testing of 25 volunteers (17 males and 8 females) demonstrated that their method could be effective in automatic detection, segmentation and classification of the SRS from original audio recordings.
Starting Page	21
Ending Page	29
Page Count	9
ISSN	17519675
Volume Number	9
e-ISSN	17519683
Issue Number	Issue 1, Feb (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/9/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0266
Journal	IET Signal Processing
Publisher Date	2015-02-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acoustic Signal Processing Audio Recording Audio Recording Media And Technique Automatic Snore Related Signal Classification Automatic Snore Related Signal Detection Automatic Snore Related Signal Segmentation Biology And Medical Computing Biomedical Engineering Biomedical Measurement And Imaging Classifier Training Digital Signal Processing Healthcare Knowledge Engineering Technique Learning in AI Machine Learning Technique Medical Signal Detection Medical Signal Processing Obstructive Sleep Apnea-hypopnea Syndrome Patient OSAHS Overnight Audio Recording Personal Health Database Signal Classification Signal Detection Snore Obstruction Site Snore Source Site SRS Events Detection
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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