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Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification

Cited 7 time in Web of Science Cited 6 time in Scopus
Authors
Kim, Jaepil; Kim, Taehoon; Lee, Donmoon; Kim, Jeong-Whun; Lee, Kyogu
Issue Date
2017-01-07
Citation
BioMedical Engineering OnLine, 16(1):6
Keywords
Obstructive sleep apneaBreathing soundOSA severity classificationTransition probabilityCyclostationaryApnea-hypopnea index
Description
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.
Abstract
Abstract

Background
Polysomnography (PSG) is the gold standard test for obstructive sleep apnea (OSA), but it incurs high costs, requires inconvenient measurements, and is limited by a one-night test. Thus, a repetitive OSA screening test using affordable data would be effective both for patients interested in their own OSA risk and in-hospital PSG. The purpose of this research was to develop a four-OSA severity classification model using a patient’s breathing sounds.


Methods
Breathing sounds were recorded from 83 subjects during a PSG test. There was no exclusive experimental protocol or additional recording instruments use throughout the sound recording procedure. Based on the Apnea-Hypopnea Index (AHI), which indicates the severity of sleep apnea, the subjects’ sound data were divided into four-OSA severity classes. From the individual sound data, we proposed two novel methods which were not attempted in previous OSA severity classification studies. First, the total transition probability of approximated sound energy in time series, and second, the statistical properties derived from the dimension-reduced cyclic spectral density. In addition, feature selection was conducted to achieve better results with a more relevant subset of features. Then, the classification model was trained using support vector machines and evaluated using leave-one-out cross-validation.


Results
The overall results show that our classification model is better than existing multiple OSA severity classification method using breathing sounds. The proposed method demonstrated 79.52% accuracy for the four-class classification task. Additionally, it demonstrated 98.0% sensitivity, 75.0% specificity, and 92.78% accuracy for OSA subject detection classification with AHI threshold 5.


Conclusions
The results show that our proposed method can be used as part of an OSA screening test, which can provide the subject with detailed OSA severity results from only breathing sounds.
Language
English
URI
http://hdl.handle.net/10371/100356
DOI
https://doi.org/10.1186/s12938-016-0306-7
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Graduate School of Convergence Science and Technology (융합과학기술대학원)Dept. of Transdisciplinary Studies(융합과학부)Journal Papers (저널논문_융합과학부)
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