■阻塞性睡眠呼吸暂停(OSA)是一种常见的睡眠呼吸障碍,这可能会对多个人类系统造成严重损害。尽管多导睡眠图(PSG)是当前诊断的金标准,它既复杂又昂贵。因此,找到一个简单的,采用经济、快速的初筛和诊断方法代替PSG诊断OSA。
■这项研究的目的是为OSA的诊断和分类提出一种新的方法,用于自动检测睡眠呼吸暂停低通气事件(AHE)的持续时间,以便仅根据睡眠呼吸的声音信号来估计通宵AHE的总持续时间与总睡眠时间的比率(S),并确定OSA。
■我们对参与者进行了PSG测试,并提取了相关的睡眠呼吸音信号数据。本研究分两个阶段进行。在第一阶段,记录符合条件的受试者的相关PSG报告数据,提取每个受试者数据中AHE的总持续时间,计算S值以评估OSA的严重程度。在第二阶段,仅使用同一批受试者的睡眠呼吸音信号数据进行自动检测,提取睡眠呼吸音信号中的S值,并将S值与PSG诊断结果进行比较,计算实验方法的准确性。
■在225个科目中。使用PSG作为参考标准,从PSG诊断数据报告中提取的S值可以准确诊断OSA(准确率为99.56%)并区分其严重程度(准确率为95.11%)。睡眠呼吸音旌旗灯号中检测的S值在诊断重度OSA中的精确性到达100%。
■结果表明,实验参数S值在OSA诊断和分类中是可行的。OSA只能通过睡眠呼吸音来识别和评估。该方法有助于简化传统OSA的诊断分级,为后续开发简易诊断分级设备奠定基础。
UNASSIGNED: Obstructive sleep apnea (OSA) is a common sleep disordered breathing disorder, which can cause serious damage to multiple human systems. Although polysomnography (PSG) is the current gold standard for diagnosis, it is complex and expensive. Therefore, it is of great significance to find a simple, economical and rapid primary screening and diagnosis method to replace PSG for the diagnosis of OSA.
UNASSIGNED: The purpose of this study is to propose a new method for the diagnosis and classification of OSA, which is used to automatically detect the duration of sleep apnea hypopnea events (AHE), so as to estimate the ratio(S) of the total duration of all-night AHE to the total sleep time only based on the sound signal of sleep respiration, and to identify OSA.
UNASSIGNED: We performed PSG tests on participants and extracted relevant sleep breathing sound signal data. This study is carried out in two stages. In the first stage, the relevant PSG report data of eligible subjects were recorded, the total duration of AHE in each subject\'s data was extracted, and the S value was calculated to evaluate the severity of OSA. In the second stage, only the sleep breath sound signal data of the same batch of subjects were used for automatic detection, and the S value in the sleep breath sound signal was extracted, and the S value was compared with the PSG diagnosis results to calculate the accuracy of the experimental method.
UNASSIGNED: Among 225 subjects. Using PSG as the reference standard, the S value extracted from the PSG diagnostic data report can accurately diagnose OSA(accuracy rate 99.56%) and distinguish its severity (accuracy rate 95.11%). The accuracy of the S value detected in the sleep breathing sound signal in the diagnosis of severe OSA reached 100%.
UNASSIGNED: The results show that the experimental parameter S value is feasible in OSA diagnosis and classification. OSA can be identified and evaluated only by sleep breathing sounds. This method helps to simplify the diagnostic grading of traditional OSA and lays a foundation for the subsequent development of simple diagnostic grading equipment.