CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Institute for Bioengineering of Catalonia and Deptartment ESAII, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
Respiration. 2013;85(4):312-8. doi: 10.1159/000342010. Epub 2012 Sep 11.
The identification of obstructive and central hypopneas is considered challenging in clinical practice. Presently, obstructive and central hypopneas are usually not differentiated or scores lack reliability due to the technical limitations of standard polysomnography. Esophageal pressure measurement is the gold-standard for identifying these events but its invasiveness deters its usage in daily practice.
To determine the feasibility and efficacy of an automatic noninvasive analysis method for the differentiation of obstructive and central hypopneas based solely on a single-channel nasal airflow signal. The obtained results are compared with gold-standard esophageal pressure scores.
A total of 41 patients underwent full night polysomnography with systematic esophageal pressure recording. Two experts in sleep medicine independently differentiated hypopneas with the gold-standard esophageal pressure signal. Features were automatically extracted from the nasal airflow signal of each annotated hypopnea to train and test the automatic analysis method. Interscorer agreement between automatic and visual scorers was measured with Cohen's kappa statistic (ĸ).
A total of 1,237 hypopneas were visually differentiated. The automatic analysis achieved an interscorer agreement of ĸ = 0.37 and an accuracy of 69% for scorer A, ĸ = 0.40 and 70% for scorer B and ĸ = 0.41 and 71% for the agreed scores of scorers A and B.
The promising results obtained in this pilot study demonstrate the feasibility of noninvasive single-channel hypopnea differentiation. Further development of this method may help improving initial diagnosis with home screening devices and offering a means of therapy selection and/or control.
在临床实践中，识别阻塞性和中枢性呼吸暂停是具有挑战性的。目前，由于标准多导睡眠图的技术限制，阻塞性和中枢性呼吸暂停通常无法区分或评分缺乏可靠性。食管压力测量是识别这些事件的金标准，但由于其侵入性，妨碍了其在日常实践中的应用。
确定一种基于单通道鼻气流信号自动无创分析方法区分阻塞性和中枢性呼吸暂停的可行性和有效性。将获得的结果与金标准食管压力评分进行比较。
共 41 例患者行整夜多导睡眠图检查，并进行系统食管压力记录。两位睡眠医学专家独立使用金标准食管压力信号对呼吸暂停进行区分。从每个标记的呼吸暂停的鼻气流信号中自动提取特征，以训练和测试自动分析方法。自动分析者和视觉分析者之间的评分者间一致性采用 Cohen's kappa 统计量（ĸ）进行测量。
共对 1237 次呼吸暂停进行了视觉区分。自动分析的评分者间一致性为ĸ=0.37，准确性为 69%，对于评分者 A；ĸ=0.40，准确性为 70%，对于评分者 B；以及评分者 A 和 B 一致的得分，ĸ=0.41，准确性为 71%。
这项初步研究中获得的有希望的结果表明，非侵入性单通道呼吸暂停区分是可行的。这种方法的进一步发展可能有助于提高家庭筛查设备的初始诊断，并提供一种治疗选择和/或控制的方法。
