Noninvasive blood glucose sensing by near-infrared spectroscopy based on PLSR combines SAE deep neural network approach
Noninvasive blood glucose sensing by near-infrared spectroscopy based on PLSR combines SAE deep neural network approach
复制标题
基于PLSR的近红外光谱无创血糖传感结合SAE深度神经网络方法
DOI：
10.1016/j.infrared.2020.103620
复制
发表时间：
2021-01-13
影响因子：
3.3
通讯作者：
Zhao, Zhe
中科院分区：
文献类型：
--
作者：
Han, Guang;Chen, Siqi;Zhao, Zhe
近红外光谱技术被认为是无创血糖检测的最有效方法之一。由于人体组织的强散射和个体间的差异，光谱数据与血糖浓度之间的关系是非线性的。因此，线性预测模型在对多个人体样本建模时具有局限性。本文提出了一种混合模型，以提高预测精度和通用性的方法，这是基于集成的线性偏最小二乘回归（PLSR）与非线性堆叠自动编码器（SAE）深度神经网络。在这项工作中，手掌的漫反射光谱测量在六个不同的波长在19个健康受试者。多个样本的预测结果表明，PLSRSAE模型的相关系数平均从0.3021提高到0.9216，与传统的PLSR模型相比，预测效果明显优化。此外，支持向量回归（SVR）模型和PLSR-SAE模型的预测精度分别为0.8243和0.9216。此外，在Clarke误差网格分析中，PLSR-SAE模型在A区的点达到97.96%，表明该无创血糖检测方法的预测精度可以达到临床实验室标准的精度范围。此外，它还显示了将线性和非线性回归模型相结合用于其他血液成分非侵入性预测的潜力。
Near-infrared spectroscopy has been considered as one of the most effective methods for noninvasive blood glucose sensing. Due to the strong scattering of human tissues and the differences among individuals, the relationship between spectral data and blood glucose concentration is nonlinear. Therefore, the linear prediction model has limitations when modeling multiple human samples. The present paper proposes a hybrid model in order to improve the prediction accuracy and versatility of the method, which was based on integrated linear partial least square regression (PLSR) with the nonlinear stacked auto-encoder (SAE) deep neural network. In this work, the diffuse reflectance spectrum of the palm was measured at six different wavelengths in 19 healthy subjects. The prediction results of multiple samples demonstrated that the correlation coefficients of the PLSRSAE model is improved from 0.3021 to 0.9216 on average, which significantly optimizes the prediction effect compared with the traditional PLSR model. In addition, the prediction accuracy of Support Vector Regression (SVR) model and PLSR-SAE model are 0.8243 and 0.9216 respectively. Furthermore, in Clarke error grid analysis, the PLSR-SAE model could achieve 97.96% of points in A region, which has demonstrated that the prediction accuracy of this noninvasive blood glucose detection method might meets the precision range of clinical laboratory standards. Furthermore, it shows the potential of combining linear and nonlinear regression models for noninvasive prediction of other blood components.