| Automatic and unsupervised snore sound extraction from respiratory sound signals. | |
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MedLine Citation:
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PMID: 20679022 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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In this paper, an automatic and unsupervised snore detection algorithm is proposed. The respiratory sound signals of 30 patients with different levels of airway obstruction were recorded by two microphones: one placed over the trachea (the tracheal microphone), and the other was a freestanding microphone (the ambient microphone). All the recordings were done simultaneously with full-night polysomnography during sleep. The sound activity episodes were identified using the vertical box (V-Box) algorithm. The 500-Hz subband energy distribution and principal component analysis were used to extract discriminative features from sound episodes. An unsupervised fuzzy C-means clustering algorithm was then deployed to label the sound episodes as either snore or no-snore class, which could be breath sound, swallowing sound, or any other noise. The algorithm was evaluated using manual annotation of the sound signals. The overall accuracy of the proposed algorithm was found to be 98.6% for tracheal sounds recordings, and 93.1% for the sounds recorded by the ambient microphone. |
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Authors:
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Ali Azarbarzin; Zahra M K Moussavi |
Publication Detail:
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Type: Journal Article Date: 2010-07-29 |
Journal Detail:
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Title: IEEE transactions on bio-medical engineering Volume: 58 ISSN: 1558-2531 ISO Abbreviation: IEEE Trans Biomed Eng Publication Date: 2011 May |
Date Detail:
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Created Date: 2011-04-22 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0012737 Medline TA: IEEE Trans Biomed Eng Country: United States |
Other Details:
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Languages: eng Pagination: 1156-62 Citation Subset: IM |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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