Morgenstern C, Schwaibold M, Randerath W, Bolz A, Jane R
Dept. ESAII, Universitat Politècnica de Catalunya (UPC), Institut de Bioenginyeria de Catalunya (IBEC) and CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Pau Gargallo 5, 08028 Barcelona, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:7102-5. doi: 10.1109/IEMBS.2009.5332900.
The differentiation between obstructive and central respiratory events is one of the most recurrent tasks in the diagnosis of sleep disordered breathing. Esophageal pressure measurement is the gold-standard method to assess respiratory effort and identify these events. But as its invasiveness discourages its use in clinical routine, non-invasisve systems have been proposed for differentiation. However, their adoption has been slow due to their limited clinical validation, as the creation of manual, gold-standard validation sets by human experts is a cumbersome procedure. In this study, a new system is proposed for an objective automatic, gold-standard differentiation between obstructive and central hypopneas with the esophageal pressure signal. First, an overall of 356 hypopneas of 16 patients were manually scored by a human expert to create a gold-standard validation set. Then, features were extracted from each hypopnea to train and test classifiers (Discriminant Analysis, Support Vector Machines and adaboost classifiers) to differentiate between central and obstructive hypopneas with the gold-standard esophageal pressure signal. The automatic differentiation system achieved promising results, with a sensitivity of 0.88, a specificity of 0.93 and an accuracy of 0.90. Hence, this system seems promising for an automatic, gold-standard differentiation between 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最常见的任务之一。食管压力测量是评估呼吸努力并识别这些事件的金标准方法。但由于其侵入性使其难以应用于临床常规检查，因此有人提出了非侵入性系统用于鉴别。然而，由于其临床验证有限，它们的采用速度一直很慢，因为由人类专家创建手动的金标准验证集是一个繁琐的过程。在本研究中，提出了一种新系统，用于根据食管压力信号对阻塞性和中枢性呼吸浅慢进行客观自动的金标准鉴别。首先，由一名人类专家对16名患者的总共356次呼吸浅慢进行人工评分，以创建一个金标准验证集。然后，从每次呼吸浅慢中提取特征，以训练和测试分类器（判别分析、支持向量机和adaboost分类器），以便根据金标准食管压力信号区分中枢性和阻塞性呼吸浅慢。自动鉴别系统取得了令人满意的结果，灵敏度为0.88，特异性为0.93，准确率为0.90。因此，该系统似乎有望用于阻塞性和中枢性呼吸浅慢的自动金标准鉴别。
