论文标题
协方差相交以提高光杀解率衍生的呼吸速率的鲁棒性
Covariance Intersection to Improve the Robustness of the Photoplethysmogram Derived Respiratory Rate
论文作者
论文摘要
可以从可穿戴设备中的光学传感器记录的光插图（PPG）估算呼吸速率（RR）。来自不同PPG功能的估计值的融合导致准确性提高，但由于丢弃了不可靠的数据而导致的最终估计数量减少了。我们提出了一种使用协方差相交的新型，可调的融合算法，以估计PPG（CIF）的RR。该算法适应可用功能估计的数量，并考虑到每个估计值的可信度。在使用Capnobase数据集和Capnography的参考RR的基准测量实验中，我们将CIF与最新的智能融合（SF）算法进行了比较。 CIF的中间均方根误差为1.4呼吸/分钟，SF的呼吸/分钟为1.8。 CIF将所有记录的保留率分布显着从0.46提高到0.90（p $ <$ 0.001）。与参考RR的一致性很高，Pearson的相关系数为0.94，偏置为0.3呼吸/分钟，一致性限制为-4.6和5.2呼吸/分钟。另外，该算法在计算上是有效的。因此，CIF可以从可穿戴PPG录音中得出更强大的RR估计。
Respiratory rate (RR) can be estimated from the photoplethysmogram (PPG) recorded by optical sensors in wearable devices. The fusion of estimates from different PPG features has lead to an increase in accuracy, but also reduced the numbers of available final estimates due to discarding of unreliable data. We propose a novel, tunable fusion algorithm using covariance intersection to estimate the RR from PPG (CIF). The algorithm is adaptive to the number of available feature estimates and takes each estimates' trustworthiness into account. In a benchmarking experiment using the CapnoBase dataset with reference RR from capnography, we compared the CIF against the state-of-the-art Smart Fusion (SF) algorithm. The median root mean square error was 1.4 breaths/min for the CIF and 1.8 breaths/min for the SF. The CIF significantly increased the retention rate distribution of all recordings from 0.46 to 0.90 (p $<$ 0.001). The agreement with the reference RR was high with a Pearson's correlation coefficient of 0.94, a bias of 0.3 breaths/min, and limits of agreement of -4.6 and 5.2 breaths/min. In addition, the algorithm was computationally efficient. Therefore, CIF could contribute to a more robust RR estimation from wearable PPG recordings.
