Hossen A, Jaju D, Al-Ghunaimi B, Al-Faqeer B, Al-Yahyai T, Hassan M O, Al-Abri M
Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman.
Technol Health Care. 2013;21(4):291-303. doi: 10.3233/THC-130724.
Obstructive Sleep Apnea (OSA) is the cessation of breathing during sleep due to the collapse of upper airway. Polysomnographic recording is a conventional method for detection of OSA. Although it provides reliable results, it is expensive and cumbersome. Thus, an advanced non-invasive signal processing based technique is needed.
The main purpose of this work is to predict the severity of sleep apnea using an efficient wavelet-based spectral analysis method of the heart rate variability (HRV) to classify sleep apnea into three different levels (mild, moderate, and severe) according to its severity and to distinguish them from normal subjects.
The standard FFT spectrum analysis method and the soft-decision wavelet-based technique are to be used in this work in order to rank patients to full polysomnography. Data of 20 normal subjects and 20 patients with mild apnea and 20 patients with moderate apnea and 20 patients of severe apnea are used in this study. The data is obtained from the sleep laboratory of Sultan Qaboos University hospital in Oman. Four different classification versions have been used in this work.
Accuracy result of 90% was obtained between severe and normal subjects and 85% between mild and normal and 75% between severe and moderate and 83.75% between normal and patients.
The VLF/LF power spectral ratio of the wavelet-based soft-decision analysis of the RRI data after a high-pass filter resulted in the best accuracy of classification in all versions.
阻塞性睡眠呼吸暂停（OSA）是指睡眠期间由于上呼吸道塌陷导致的呼吸停止。多导睡眠图记录是检测OSA的传统方法。尽管它能提供可靠的结果，但费用高昂且操作繁琐。因此，需要一种先进的基于非侵入性信号处理的技术。
这项工作的主要目的是使用一种高效的基于小波的心率变异性（HRV）频谱分析方法来预测睡眠呼吸暂停的严重程度，根据其严重程度将睡眠呼吸暂停分为三个不同级别（轻度、中度和重度），并将它们与正常受试者区分开来。
在这项工作中，将使用标准的快速傅里叶变换（FFT）频谱分析方法和基于软决策小波的技术，以便对患者进行全多导睡眠图排序。本研究使用了20名正常受试者、20名轻度呼吸暂停患者、20名中度呼吸暂停患者和20名重度呼吸暂停患者的数据。这些数据来自阿曼苏丹卡布斯大学医院的睡眠实验室。这项工作使用了四种不同的分类版本。
重度与正常受试者之间的准确率为90%，轻度与正常受试者之间为85%，重度与中度之间为75%，正常与患者之间为83.75%。
对经高通滤波器处理后的RRI数据进行基于小波软决策分析的VLF/LF功率谱比在所有版本中分类准确率最高。
