文章基本信息

标题：A Neural Network System for Detection of Obstructive Sleep Apnea Through SpO2 Signal Features
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作者：Laiali Almazaydeh ; Miad Faezipour ; Khaled Elleithy 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2012
卷号：3
期号：5
DOI：10.14569/IJACSA.2012.030502
出版社：Science and Information Society (SAI)
摘要：Obstructive sleep apnea (OSA) is a common disorder in which individuals stop breathing during their sleep. These episodes last 10 seconds or more and cause oxygen levels in the blood to drop. Most of sleep apnea cases are currently undiagnosed because of expenses and practicality limitations of overnight polysomnography (PSG) at sleep labs, where an expert human observer is required. New techniques for sleep apnea classification are being developed by bioengineers for most comfortable and timely detection. In this study, we develop and validate a neural network (NN) using SpO2 measurements obtained from pulse oximetry to predict OSA. The results show that the NN is useful as a predictive tool for OSA with a high performance and improved accuracy, approximately 93.3%, which is better than reported techniques in the literature.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; sleep apnea; PSG; SpO2; features extraction; oximetry; neural networks.