多导睡眠图
阻塞性睡眠呼吸暂停
医学
气体分析呼吸
呼吸暂停
色谱法
内科学
化学
作者
Da-kang Yao,Li-Huan Chieng,Rayleigh Ping‐Ying Chiang
出处
期刊:Sleep
[Oxford University Press]
日期:2020-04-01
卷期号:43 (Supplement_1): A172-A172
标识
DOI:10.1093/sleep/zsaa056.447
摘要
Abstract Introduction Human exhaled breath test is getting more important for non-invasive health monitoring and detecting method nowadays. The diagnosis of obstructive sleep apnea syndrome (OSAS) is often difficult to be confirmed from the daytime presentation and usually need the overnight polysomnography. The methods for OSAS screening are therefore potentials for the clinical practice in the near future. Methods In this research, a method of thermal desorption (TD) tendon with gas chromatography-mass spectrometry (GC-MS) system has been developed for screeing of OSAS patients. We detected the volatile organic compounds (VOCs) from the special designed experimental bags which collected exhaled gas. Then we compared the VOCs from normal control and OSAS group in order to find out the biomarkers which could be used to screen OSAS patients. Furthermore, the Reliable Number(N) was used to see how often the VOC identified in all the experients in OSAS group and was defined as the times of a single VOC identified devided by the times of total experiment in a single OSAS patient. Results While the reliable number been set as ≥50%, we found 8 VOC markers, including Pentane and Cyclopentyl acetylene, appeared more often in OSAS patients. When we raise N to ≥70%, we have only 3 markers remaining. Conclusion Based on this result, we utilize the artificial intelligence method, deep learning, to figure out whether the peak intensity of different biomarkers are related to the severity of OSAS. Support Thanks for Da-Jeng Yao Lab’s support
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