摘要
现有的近红外光谱无创血糖建模方法大多是基于多波长近红外光谱信号,不利于无创血糖仪在家庭中普及,并且这些建模方法没有考虑单个个体每天血糖变化规律的差异性。针对这些问题,本文以血糖吸收最强的1 550 nm近红外光吸光度为自变量、血糖浓度为因变量,结合粒子群(PSO)算法和人工神经网络(ANN)建立了一种无创血糖检测模型——PSO-2ANN模型。该模型以两个结构和参数确定的人工神经网络为基本的子模块,通过粒子群算法优化两个子模块的权重系数得到最终的模型。使用PSO-2ANN模型对10名志愿者的实验数据进行预测。结果表明,其中9名志愿者的预测相对误差率均小于20%;通过PSO-2ANN模型得到的血糖浓度预测值分布在克拉克误差网格A、B区域的比重为98.28%,证实了PSO-2ANN模型具有比传统人工神经网络模型更为理想的预测精度和稳健性。另外,单个个体由于外界环境、心情、精神状态等因素的影响,每天血糖的变化规律可能会出现一定程度的差异性,PSO-2ANN模型只需要调节一个参数便能修正这种差异性。本文提出的PSO-2ANN模型为克服血糖浓度预测的个体差异性提供了新的思路。
Existing near-infrared non-invasive blood glucose detection modelings mostly detect multi-spectral signals with different wavelength, which is not conducive to the popularization of non-invasive glucose meter at home and does not consider the physiological glucose dynamics of individuals. In order to solve these problems, this study presented a non-invasive blood glucose detection model combining particle swarm optimization （PSO） and artificial neural network （ANN） by using the 1 550 nm near-infrared absorbance as the independent variable and the concentration of blood glucose as the dependent variable, named as PSO-2ANN. The PSO-2ANN model was based on two sub-modules of neural networks with certain structures and arguments, and was built up after optimizing the weight coefficients of the two networks by particle swarm optimization. The results of 10 volunteers were predicted by PSO-2ANN. It was indicated that the relative error of 9 volunteers was less than 20%; 98.28% of the predictions of blood glucose by PSO-2ANN were distributed in the regions A and B of Clarke error grid, which confirmed that PSO-2ANN could offer higher prediction accuracy and better robustness by comparison with ANN. Additionally, even the physiological glucose dynamics of individuals may be different due to the influence of environment, temper, mental state and so on, PSO-2ANN can correct this difference only by adjusting one argument. The PSO-2ANN model provided us a new prospect to overcome individual differences in blood glucose prediction.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2017年第5期713-720,共8页
Journal of Biomedical Engineering
基金
国家自然科学基金项目(81371713)
中央高校基本科研业务费专项(106112015CDJZR235522)
关键词
近红外光谱技术
无创血糖检测
粒子群
人工神经网络
near-infrared technique
non-invasive blood glucose detection
partical swarm optimization
artificialneural network
