Morgenstern C, Schwaibold M, Randerath W, Bolz A, Jane R
Institut de Bioenginyeria de Catalunya (IBEC), Dept. ESAII, Universitat Politècnica de Catalunya (UPC), Baldiri i Reixach 4, 08028, Barcelona, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:6142-5. doi: 10.1109/IEMBS.2010.5627787.
The differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events but its invasiveness deters its usage in clinical routine. Flattening patterns appear in the airflow signal during episodes of inspiratory flow limitation (IFL) and have been shown with invasive techniques to be useful to differentiate between central and obstructive hypopneas. In this study we present a new method for the automatic non-invasive differentiation of obstructive and central hypopneas solely with nasal airflow. An overall of 36 patients underwent full night polysomnography with systematic Pes recording and a total of 1069 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the nasal airflow signal to train and test our automatic classifier (Discriminant Analysis). Flattening patterns were non-invasively assessed in the airflow signal using spectral and time analysis. The automatic non-invasive classifier obtained a sensitivity of 0.71 and an accuracy of 0.69, similar to the results obtained with a manual non-invasive classification algorithm. Hence, flattening airflow patterns seem promising for the non-invasive differentiation of obstructive and central hypopneas.
阻塞性和中枢性呼吸事件的鉴别是睡眠呼吸障碍诊断中的一项重大挑战。食管压力（Pes）测量是识别这些事件的金标准方法，但其侵入性阻碍了其在临床常规中的应用。吸气流量受限（IFL）发作期间气流信号会出现平坦模式，并且已通过侵入性技术证明其有助于区分中枢性和阻塞性呼吸暂停低通气。在本研究中，我们提出了一种仅利用鼻气流自动无创鉴别阻塞性和中枢性呼吸暂停低通气的新方法。总共36名患者接受了整夜多导睡眠监测，并系统记录了Pes，人类专家对总共1069次呼吸暂停低通气进行了人工评分，以创建一个金标准注释集。从鼻气流信号中自动提取特征，以训练和测试我们的自动分类器（判别分析）。使用频谱和时间分析对气流信号中的平坦模式进行无创评估。自动无创分类器的灵敏度为0.71，准确率为0.69，与手动无创分类算法的结果相似。因此，气流平坦模式在阻塞性和中枢性呼吸暂停低通气的无创鉴别方面似乎很有前景。
