Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters
Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters
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DOI：
10.1109/tbme.2016.2613124
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发表时间：
2017-08-01
影响因子：
4.6
通讯作者：
Clifton, David A.
中科院分区：
文献类型：
--
作者：
Pimentel, Marco A. F.;Johnson, Alistair E. W.;Clifton, David A.
目标：用于从光电体积描记图（PPG）估计呼吸率（RR）的当前方法通常不能区分高质量输入数据和低质量输入数据的时段，并且不能在独立的“验证”数据集上良好地执行。现有方法的鲁棒性的缺乏直接导致这种系统缺乏对临床实践的渗透。本工作提出了一种替代方法，以提高从PPG的RR估计的鲁棒性。研究方法：所提出的算法是基于使用不同阶数的多个自回归模型，用于确定从PPG导出的三个扰动引起的变化（频率、幅度和强度）中的主导呼吸频率。该算法在两个不同的数据集上进行了测试，包括在不同的临床环境中从儿童和成人获得的95个8分钟PPG记录（总计），并将其使用两种窗口大小（32和64秒）的性能与文献中现有的方法进行了比较。结果如下：所提出的方法达到了与文献中现有方法相当的精度，平均绝对误差（中值，窗口大小为32秒时的第25 - 75位数）为1.5（0.3-3.3）和4.0（1.8-5.5）次呼吸/分钟（分别针对每个数据集），同时为更大比例的窗口提供RR估计（保留90%以上的输入数据）。结论：所提出的方法增加了RR估计的鲁棒性。重要性：这项工作表明，使用大型公开数据集对于提高临床实践中使用的可穿戴监测算法的鲁棒性至关重要。
Goal: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. Methods: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. Results: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25th-75th percentiles for a window size of 32 seconds) of 1.5 (0.3-3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). Conclusion: Increased robustness of RR estimation by the proposed method was demonstrated. Significance: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice.