在传统的血氧饱和度测量方法中, 首先计算对数光电容积脉搏波交、 真流成分的比值(R值), 然后通过实验建立血氧饱和度值与R值的线性回归模型, 旨在消除光程的影响, 将个体差异的影响降至最小化, 是一种基于Lambert-Beer定律的降维处理。 当考虑散射时, 这种以交、 直流成分的比值作为自变量的降维处理方式损失了信息量, 引入了系统误差, 限制了测量的精度。 为了减小这种影响, 从透射光电脉搏波所含信息量的角度, 提出了使用双波长对数光电容积脉搏波的峰值和谷值作为自变量与血氧饱和度值建立线性回归模型的新方法。 从23名健康志愿者中获得133例光电脉搏波及血氧饱和度参考值样本, 从中随机抽取90例作为建模集, 另外43例作为预测集, 对比了新方法和R值法的建模和预测结果。 随机抽取建模集并预测10次, 新方法和R值法的平均预测相关系数分别为0.890 6和0.846 8, 平均预测均方根误差分别为0.889 6%和1.037 3%, 结果表明新方法的预测效果优于传统R值法, 使用四个特征量建立血氧饱和度预测模型, 能够提高测量的稳定性和精度, 对基于有限波长光谱数据建模测量人体血液生理参数有指导意义。

The traditional method of measuring arterial oxygen saturation is that R value, the ratio of alternating component of the logarithmic photoplethysmography, is firstly computed and then the linear regression model is established by experiment. The R value computation is a dimension reduction process based on Lambert-beer law, which aims at eliminating the influence of optical path andminimizing the impact of individual differences. When taking scattering into consideration, the dimension reduction process loses information, introduces the system error and limits the precision of measurement. In order to reduce the measurement error resulting from the scattering effects, this paper presents a new method that the peak and valley values of dual-wavelength logarithmic photoplethysmography waves are used as the independent variables to develop a linear regression model to predict the arterial oxygen saturation. During the experiment, the in-vivo measurements were carried out on 23 healthy volunteer and 133 samples of photoplethysmography waves and the reference value of oxygen saturation were recorded. To compare the predictive performance between the new method and the R value method, 90 samples were randomly selected as modeling sets and the remaining 43 samples were used as prediction sets. Random selection of modeling sets and prediction are executed 10 times. The average related coefficients of the prediction sets of the new method and the R value method are 0.890 6 and 0.846 8, and the average root mean square errors are 0.889 6% and 1.037 3% respectively. Results indicate that the performance of the new method is better than the one of the R value method, and the predictivemodel based on 4 parameters can improve the stability and accuracy of measurement. And the new method has guiding significance to the measurement of human body’s blood physiological information based on limited wavelength spectrum data.