Sleep apnea (SA) syndrome is a respiratory disorder that occurs during the sleep. Polysomnography (PSG) has been widely applied by clinicians as a gold standard in the clinical diagnosis of SA syndrome. However, the use of PSG is inconvenient, intrusive, and significantly affects the sleep quality of patient. In this paper, we provide a nonintrusive solution for SA detection. Specifically, a force sensor was employed for the noninvasive vital sign acquisition during the patient’s sleep, where the respiratory signal was extracted adaptively by using the morphological filter. It was observed that the morphological variations before and during the occurrence of the SA events were significant for the SA discrimination. By taking advantage of the differential features with respect to the respiratory signal, the recognition of the SA event was performed using classifiers. For validation, the all-night PSG recordings of 12 volunteers with 8 SA syndrome patients were obtained from the National Clinical Research Center for Respiratory Disease. Numerical results showed that the proposed scheme achieved an averaged accuracy, sensitivity and specificity of 83.67%, 58.57% and 85.13%, respectively, for the SA recognition.

中文翻译：

---

监测非侵入性生命体征以检测睡眠呼吸暂停

睡眠呼吸暂停 (SA) 综合征是一种在睡眠期间发生的呼吸系统疾病。多导睡眠图 (PSG) 已被临床医生广泛用作 SA 综合征临床诊断的金标准。然而，PSG的使用不方便，具有侵入性，并且显着影响患者的睡眠质量。在本文中，我们为 SA 检测提供了一种非侵入式解决方案。具体而言，在患者睡眠期间采用力传感器进行无创生命体征采集，其中通过使用形态滤波器自适应地提取呼吸信号。观察到 SA 事件发生之前和期间的形态变化对 SA 判别具有显着意义。通过利用关于呼吸信号的差异特征，SA事件的识别是使用分类器进行的。为了验证，从国家呼吸疾病临床研究中心获得了 12 名志愿者和 8 名 SA 综合征患者的通宵 PSG 记录。数值结果表明，所提出的方案对 SA 识别的平均准确度、灵敏度和特异性分别为 83.67%、58.57% 和 85.13%。