论文标题
使用血氧饱和信号自动对呼吸暂停和脑呼吸呼吸症事件进行自动评分
Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals
论文作者
论文摘要
阻塞性睡眠呼吸暂停呼吸症（OSAH）综合征是一种非常常见且经常无法诊断的睡眠障碍。它的特征是在睡觉时反复发生部分（呼吸呼吸症）或总呼吸道障碍物（呼吸暂停）。这项研究利用一种用于多类结构词典学习的先前开发的方法称为DAS-KSVD，以仅使用血氧饱和信号自动检测呼吸暂停和呼吸呼吸道的单个事件。该方法使用合并的判别度量，该度量能够有效地量化字典中每个原子的可区分性程度。使用从睡眠心脏健康研究数据库获得的信号来检测和分类DAS-KSVD来检测和分类呼吸暂停和呼吸呼吸道事件。对于中度至重度OSAH筛选，结果的接收器工作特性曲线分析显示了曲线下的面积为0.957，诊断灵敏度和特异性分别为87.56％和88.32％。与大多数最新程序相比，这些结果代表了改进。因此，该方法可用于更可靠，方便地筛查OSAH综合征，仅使用脉搏血氧仪。
The obstructive sleep apnea-hypopnea (OSAH) syndrome is a very common and frequently undiagnosed sleep disorder. It is characterized by repeated events of partial (hypopnea) or total (apnea) obstruction of the upper airway while sleeping. This study makes use of a previously developed method called DAS-KSVD for multiclass structured dictionary learning to automatically detect individual events of apnea and hypopnea using only blood oxygen saturation signals. The method uses a combined discriminant measure which is capable of efficiently quantifying the degree of discriminability of each one of the atoms in a dictionary. DAS-KSVD was applied to detect and classify apnea and hypopnea events from signals obtained from the Sleep Heart Health Study database. For moderate to severe OSAH screening, a receiver operating characteristic curve analysis of the results shows an area under the curve of 0.957 and diagnostic sensitivity and specificity of 87.56% and 88.32%, respectively. These results represent improvements as compared to most state-of-the-art procedures. Hence, the method could be used for screening OSAH syndrome more reliably and conveniently, using only a pulse oximeter.
