Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification
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- 作者：Jaepil Kim ; Taehoon Kim ; Donmoon Lee ; Jeong-Whun Kim…
- 关键词：Obstructive sleep apnea ; Breathing sound ; OSA severity classification ; Transition probability ; Cyclostationary ; Apnea ; hypopnea index
- 刊名：BioMedical Engineering OnLine
- 出版年：2017
- 出版时间：December 2017
- 年：2017
- 卷：16
- 期：1
- 全文大小：1683KB
- 刊物类别：Engineering
- 刊物主题：Biomedical Engineering; Biomaterials; Biotechnology; Biomedical Engineering/Biotechnology;
- 出版者：BioMed Central
- ISSN：1475-925X
- 卷排序：16
文摘
BackgroundPolysomnography (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.