中文摘要：

将经验模态分解（EMD）算法结合动态光谱理论中的频域提取算法用于血红蛋白浓度的无创测量。在体采集57例光电容积脉搏波，选取636．98～1086．86 nm范围内的光谱数据进行分析。首先通过EMD方法分别对各个样本每个波长的光电容积脉搏波进行去噪预处理，再利用离散傅里叶变换提取脉搏波的峰峰值构成动态光谱，最后运用偏最小二乘方法对各样本的动态光谱和血红蛋白浓度建立模型。与未经EMD处理的数据建模结果相比，EMD处理后，血红蛋白浓度预测集的相关系数从0．8798提高到0．9176，预测集均方根误差从6．6759 g＆#183;L-1减小到5．3001 g＆#183;L-1，相对误差从8．45％减小到6．71％，建模精度有了较大的提高。结果表明，采取经验模态分解的算法进行光电采集数据的去噪预处理可以提高光谱数据的信噪比，进而可以提高血液成分无创测量的准确性。

英文摘要：

Empirical mode decomposition （EMD）algorithm combined with the theory of dynamic spectrum extraction at frequen-cy domain was applied to the noninvasive measurement of hemoglobin concentration.Fifty seven cases’photoplethysmography was collected in the range of 636. 98~1 086. 86 nm in vivo.After the denoising preprocess through the EMD method for each wavelength pulse wave of each sample separately,dynamic spectrum of each sample was made up of all peaks extracted by Fou-rier transform.Partial least squares regression model was used to establish the calibration and prediction of hemoglobin concen-tration.Compared to the modeling results without EMD,the correlation coefficient of predicted values and the real values was increased from 0. 879 8 up to 0. 917 6.The root mean square error of prediction set was reduced from 6. 675 9 to 5. 300 1 g＆#183;L-1 and the relative error was reduced from 8. 45% to 6. 71%.The modeling accuracy has been greatly improved.The results showed that EMD algorithm can be effectively applied to denoise the spectral data and improve the accuracy of the non-invasive measurement of blood components.