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Received:November 25, 2015 Revised:December 31, 2015
Received:November 25, 2015 Revised:December 31, 2015
中文摘要: 为了实现无创血糖浓度检测，提出了基于支持向量机回归模型的无创血糖光谱算法. 该算法使用光电容积脉搏波（PPG）设备对志愿者指端红光、红外光交替采样得到PPG信号，然后通过微创血糖仪测得血糖浓度. 对采集到的PPG信号进行处理提取特征组成特征矩阵，分别运用不同机器学习模型对特征矩阵和实时血糖浓度进行回归训练，得到特征矩阵与血糖浓度间的关系，并对训练得到的函数关系进行验证，选取出高斯核支持向量机模型为最佳训练模型. 实验证明，与偏最小二乘回归进行对比，本文提出的运用核函数为高斯核的支持向量机算法的预测准确度能提升10%～15%，预测的高低血糖正确率达到98%.
Abstract:To achieve sensing non-invasive blood glucose concentration, this paper proposes a non-invasive blood glucose spectrum algorithm based on support vector machine (SVM) model. The algorithm firstly uses a PPG device to sample red and infrared signal of volunteers to obtain the PPG signal, and then extracts the blood glucose concentration by a minimally-invasive glucometer. Compared with traditional near-infrared spectrum and regression method, the proposed algorithm could effectively improve the prediction accuracy by adopting the SVM model to find regression relationship between signal and glycemic. And the SVM model uses the Gauss-function as the kernel function. The algorithm is independently of the individual and environmental factors. The experimental results demonstrate that compared to partial least squares regression, the proposed algorithm could improve the predictive accuracy by 10%–15%. And the algorithm's forecast accuracy could reach to 98%.
keywords: support vector machines non-invasive blood glucose measuring near-infrared spectrum photoplethysmograph
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基金项目:“核高基”专项（2012ZX01029_001_002）
引用文本：
马爽,蒲宝明.基于支持向量机的无创血糖光谱算法.计算机系统应用,2016,25(8):120-124
MA Shuang,PU Bao-Ming.Non-Invasive Blood Glucose Measurement Based on Support Vector Machine Algorithm.COMPUTER SYSTEMS APPLICATIONS,2016,25(8):120-124
马爽,蒲宝明.基于支持向量机的无创血糖光谱算法.计算机系统应用,2016,25(8):120-124
MA Shuang,PU Bao-Ming.Non-Invasive Blood Glucose Measurement Based on Support Vector Machine Algorithm.COMPUTER SYSTEMS APPLICATIONS,2016,25(8):120-124
