School of Engineering and Information Technology, University of Technology, Sydney, New South Wales, Australia.
School of Electrical and Information Engineering, University of Sydney, Sydney, New South Wales, Australia.
J Appl Physiol (1985). 2017 Oct 1;123(4):781-789. doi: 10.1152/japplphysiol.00726.2016. Epub 2017 May 25.
The forced oscillation technique (FOT) can provide unique and clinically relevant lung function information with little cooperation with subjects. However, FOT has higher variability than spirometry, possibly because strategies for quality control and reducing artifacts in FOT measurements have yet to be standardized or validated. Many quality control procedures rely on either simple statistical filters or subjective evaluation by a human operator. In this study, we propose an automated artifact removal approach based on the resistance against flow profile, applied to complete breaths. We report results obtained from data recorded from children and adults, with and without asthma. Our proposed method has 76% agreement with a human operator for the adult data set and 79% for the pediatric data set. Furthermore, we assessed the variability of respiratory resistance measured by FOT using within-session variation (wCV) and between-session variation (bCV). In the asthmatic adults test data set, our method was again similar to that of the manual operator for wCV (6.5 vs. 6.9%) and significantly improved bCV (8.2 vs. 8.9%). Our combined automated breath removal approach based on advanced feature extraction offers better or equivalent quality control of FOT measurements compared with an expert operator and computationally more intensive methods in terms of accuracy and reducing intrasubject variability. The forced oscillation technique (FOT) is gaining wider acceptance for clinical testing; however, strategies for quality control are still highly variable and require a high level of subjectivity. We propose an automated, complete breath approach for removal of respiratory artifacts from FOT measurements, using feature extraction and an interquartile range filter. Our approach offers better or equivalent performance compared with an expert operator, in terms of accuracy and reducing intrasubject variability.
强迫振荡技术（FOT）可以提供独特且与临床相关的肺功能信息，而无需受试者进行太多配合。然而，FOT 的变异性比肺活量计更高，这可能是因为 FOT 测量中的质量控制和减少伪影的策略尚未标准化或验证。许多质量控制程序依赖于简单的统计滤波器或人工操作员的主观评估。在这项研究中，我们提出了一种基于流量剖面阻力的自动去除伪影方法，应用于完整的呼吸。我们报告了从患有和不患有哮喘的儿童和成人记录的数据中获得的结果。我们提出的方法对于成人数据集的准确性为 76%，对于儿科数据集的准确性为 79%，与人工操作员具有一致性。此外，我们使用会话内变异（wCV）和会话间变异（bCV）评估了 FOT 测量的呼吸阻力的可变性。在哮喘成人测试数据集，我们的方法再次与手动操作员的 wCV 相似（6.5%对 6.9%），并且显著改善了 bCV（8.2%对 8.9%）。我们提出的基于先进特征提取的自动呼吸去除方法与专家操作员和计算密集型方法相比，在准确性和降低个体内变异性方面提供了更好或等效的 FOT 测量质量控制。强迫振荡技术（FOT）在临床测试中越来越被接受；然而，质量控制策略仍然高度可变，需要高度的主观性。我们提出了一种使用特征提取和四分位距滤波器从 FOT 测量中去除呼吸伪影的自动、完整呼吸方法。我们的方法在准确性和降低个体内变异性方面与专家操作员相比具有更好或等效的性能。
