| Automatic breath and snore sounds classification from tracheal and ambient sounds recordings. | |
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MedLine Citation:
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PMID: 20674455 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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In this study respiratory sound signals were recorded from 23 patients suspect of obstructive sleep apnea, who were referred for the full-night sleep lab study. The sounds were recorded with two microphones simultaneously: one placed over trachea and one hung in the air in the vicinity of the patient. During recording the sound signals, patients' Polysomnography (PSG) data were also recorded simultaneously. An automatic method was developed to classify breath and snore sound segments based on their energy, zero crossing rate and formants of the sound signals. For every sound segment, the number of zero crossings, logarithm of the signal's energy and the first formant were calculated. Fischer Linear Discriminant was implemented to transform the 3-dimensional (3D) feature set to a 1-dimensional (1D) space and the Bayesian threshold was applied on the transformed features to classify the sound segments into either snore or breath classes. Three sets of experiments were implemented to investigate the method's performance for different training and test data sets extracted from different neck positions. The overall accuracy of all experiments for tracheal recordings were found to be more than 90% in classifying breath and snore sounds segments regardless of the neck position. This implies the method's accuracy is insensitive to patient's position; hence, simplifying data analysis for an entire night recording. The classification was also performed on sounds signals recorded simultaneously with an ambient microphone and the results were compared with those of the tracheal recording. |
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Authors:
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Azadeh Yadollahi; Zahra Moussavi |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't Date: 2010-07-31 |
Journal Detail:
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Title: Medical engineering & physics Volume: 32 ISSN: 1873-4030 ISO Abbreviation: Med Eng Phys Publication Date: 2010 Nov |
Date Detail:
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Created Date: 2010-11-01 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9422753 Medline TA: Med Eng Phys Country: England |
Other Details:
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Languages: eng Pagination: 985-90 Citation Subset: IM |
Copyright Information:
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Copyright © 2010. Published by Elsevier Ltd. |
Affiliation:
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Department of Electrical and Computer Engineering, University of Manitoba, Chancelor St., Winnipeg, MB, Canada R3T 5V6; Telecommunication Research Labs (TRLabs), Winnipeg, MB, Canada R3T 6A8. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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