基于光学测量的无创血糖检测是目前生物医学领域的研究热点,但由于存在信噪比低、背景噪声干扰、准确度不高等问题,该无创血糖检测方法还停留在实验阶段,无法应用于临床实践。针对这些问题,提出了一种基于可见光图像的无创血糖检测方法。该方法通过采集到的散射图像,采用梯度增强决策树算法,建立了散射图像特征参量与血糖浓度关系的回归模型,并通过仿体实验验证了模型的准确性。实验结果表明,利用梯度增强回归模型,可以对可见光散射图像与葡萄糖浓度的关系进行建模,一致性决定系数可达0.929,葡萄糖检测精度平均绝对误差为0.156 g·L -1。

Non-invasive blood glucose detection based on optical measurement is a research hotspot in the biomedical field at present. However, due to the problems of low signal-to-noise ratio, background noise interference and low accuracy, the non-invasive blood glucose detection method is still in the experimental stage and cannot be applied in clinical practice. To solve these problems, a non-invasive blood glucose detection method based on visible image is proposed. By using the collected scattering images and the gradient boosting decision tree algorithm, the regression model of the relationship between the characteristic parameters of the scattering images and the blood glucose concentration is established, and the accuracy of the model is verified by the phantom experiment. Experimental results show that the relationship between visible scattering images and glucose concentration can be modeled by the gradient boosting regression model, with a consistency determination coefficient up to 0.929 and an average absolute error of glucose detection accuracy of 0.156 g·L ^-1.