基于支持向量回归的血红蛋白浓度无创检测模型

Non-invasive detection model for hemoglobin concentration based on support vector regression

为实现血红蛋白浓度的无创检测,设计基于支持向量回归的血红蛋白浓度检测方法。首先,基于Beer-Lambert定律建立血红蛋白无创检测数学模型;然后,对采集的光电容积脉搏波描记法(PPG)信号进行降噪和滤除基线漂移处理后提取出血红蛋白浓度特征信息,并使用递归特征消除算法对提取的特征信息进行选择,以去除冗余特征;最后,将筛选出的29个特征信息作为回归模型的输入特征,并采用支持向量回归算法构建血红蛋白预测回归模型。通过对249例临床数据进行试验验证(其中199例作为训练数据集,50例作为测试数据集),得出预测值与参考值的均方根误差为1.83 g/dL,相关系数为0.75(P<0.01),试验结果表明本文方法与传统有创检测方法具有较强的一致性。

To achieve non-invasive detection of hemoglobin concentration,a hemoglobin concentration detection method based on support vector regression is designed.A mathematical model for non-invasive hemoglobin detection is established based on the Beer-Lambert law.After removing the noise and baseline drift from the collected photoplethysmography signals,hemoglobin concentration information is extracted,and a recursive feature elimination algorithm is used to select the extracted information and eliminate redundant features.Finally,29 key features are identified as input to construct a hemoglobin prediction model using support vector regression algorithm.Experimental validation is conducted on 249 clinical data samples(199 cases in training dataset and 50 in test dataset),resulting in a root mean square error of 1.83 g/dL between predicted values and references,with a correlation coefficient of 0.75(P<0.01),demonstrating the high consistency of the proposed method and traditional invasive detection methods.