Pham Thuy T, Thamrin Cindy, Robinson Paul D, McEwan Alistair L, Leong Philip H W
Department of Electrical and Information Engineering, The University of Sydney, Sydney, N.S.W., Australia.
Woolcock Institute of Medical Research.
IEEE Trans Biomed Eng. 2017 Aug;64(8):1679-1687. doi: 10.1109/TBME.2016.2554599. Epub 2016 Apr 15.
Respiratory artefact removal for the forced oscillation technique can be treated as an anomaly detection problem. Manual removal is currently considered the gold standard, but this approach is laborious and subjective. Most existing automated techniques used simple statistics and/or rejected anomalous data points. Unfortunately, simple statistics are insensitive to numerous artefacts, leading to low reproducibility of results. Furthermore, rejecting anomalous data points causes an imbalance between the inspiratory and expiratory contributions.
From a machine learning perspective, such methods are unsupervised and can be considered simple feature extraction. We hypothesize that supervised techniques can be used to find improved features that are more discriminative and more highly correlated with the desired output. Features thus found are then used for anomaly detection by applying quartile thresholding, which rejects complete breaths if one of its features is out of range. The thresholds are determined by both saliency and performance metrics rather than qualitative assumptions as in previous works.
Feature ranking indicates that our new landmark features are among the highest scoring candidates regardless of age across saliency criteria. F1-scores, receiver operating characteristic, and variability of the mean resistance metrics show that the proposed scheme outperforms previous simple feature extraction approaches. Our subject-independent detector, 1IQR-SU, demonstrated approval rates of 80.6% for adults and 98% for children, higher than existing methods.
Our new features are more relevant. Our removal is objective and comparable to the manual method.
This is a critical work to automate forced oscillation technique quality control.
用于强迫振荡技术的呼吸伪影去除可被视为一个异常检测问题。目前手动去除被认为是金标准，但这种方法既费力又主观。大多数现有的自动化技术使用简单统计方法和/或剔除异常数据点。不幸的是，简单统计方法对众多伪影不敏感，导致结果的可重复性较低。此外，剔除异常数据点会导致吸气和呼气贡献之间的不平衡。
从机器学习的角度来看，此类方法是无监督的，可被视为简单的特征提取。我们假设可以使用监督技术来找到改进的特征，这些特征更具区分性且与期望输出的相关性更高。然后通过应用四分位数阈值化将如此找到的特征用于异常检测，如果一个呼吸的某个特征超出范围，则剔除整个呼吸。阈值由显著性和性能指标共同确定，而不是像以往工作那样基于定性假设。
特征排名表明，无论年龄如何，我们新的标志性特征在显著性标准方面都是得分最高的候选特征之一。F1分数、受试者工作特征曲线以及平均阻力指标的变异性表明，所提出的方案优于先前的简单特征提取方法。我们的独立于受试者的检测器1IQR - SU在成人中的认可率为80.6%，在儿童中为98%，高于现有方法。
我们的新特征更具相关性。我们的去除方法是客观的，且与手动方法相当。
这是一项实现强迫振荡技术质量控制自动化的关键工作。
