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Feature Selection Method for Estimating Systolic Blood Pressure Using the Taguchi Method

机译：Taguchi法估算收缩压的特征选择方法

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摘要

This paper proposes a method for selecting photoplethysmogram features for estimating systolic blood pressure (SBP), which uses the signal-to-noise ratio (SNR) obtained from the Taguchi method. Cuffless estimation of blood pressure using just one photoplethysmograph was carried out based on the photoplethysmogram features. However, these features are highly vulnerable to noise generated by sources such as individual variability, resulting from factors such as age and gender. Therefore, features need to be robust against such noise for accurate estimation of blood pressure. In this study, we used an orthogonal array and the SNR from the Taguchi method for selecting features of multiple regression analysis that are robust against noise. We estimated SBP from two datasets by applying the proposed feature selection method to multiple regression analysis. The obtained results show that the correlation coefficients between the actual measurement values and the estimated values were 0.88 and 0.87 for datasets 1 and 2, which are higher than those of similar methods used for comparison purposes. This confirms the effectiveness of the proposed method in estimating SBP.
机译：本文提出了一种用于选择光电容积描记特征来估计收缩压（SBP）的方法，该方法使用了Taguchi方法获得的信噪比（SNR）。基于光体积描记图特征，仅使用一部光体积描记仪进行血压的无袖估计。但是，这些功能极易受到诸如年龄和性别等因素导致的个体可变性等来源产生的噪声的影响。因此，为了准确估计血压，需要对这些噪声具有鲁棒性。在本研究中，我们使用正交阵列和Taguchi方法的SNR来选择对噪声具有鲁棒性的多元回归分析特征。通过将建议的特征选择方法应用于多元回归分析，我们从两个数据集中估算了SBP。获得的结果表明，数据集1和2的实际测量值与估计值之间的相关系数高于用于比较目的的相似方法的相关系数。这证实了所提方法在估计SBP中的有效性。

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