基于小波去噪的神经网络软测量在血糖浓度估计中的应用

由于皮下间隙液葡萄糖的易测性和测量过程中传感器感染血液的低风险性, 皮下间隙液一直是血糖监测的首选位置. 但皮下间隙液葡萄糖浓度的变化总是滞后于血糖浓度的变化, 而且测量过程中会引入噪声, 不能准确地估测血糖值, 针对这一问题提出了一种基于小波去噪的神经网络软测量方法. 该方法先对与血糖相关的一些辅助变量进行去噪处理, 然后用来训练神经网络, 建立血糖软测量模型. 通过对1号、2号成年人采集的仿真数据进行实验, 结果表明, 运用该方法得到的测量结果比皮下间隙液葡萄糖值具有更小的均方根误差、更好的信噪比、以及更小的测量延时.

Application of Neutral Network Soft-Sensing Based on Wavelet Denoising in the Blood Glucose Concentration Estimation

Subcutaneous interstitial fluid continues to be the preferred site for glucose sensing due to its easy access and lower risk of infection than that of the blood stream. But changes in subcutaneous interstitial fluid glucose are delayed with respect to changes in blood glucose. Besides, the sampling signals are inevitably influenced by noise in the measurement process. For the reasons above, a neural network soft-sensing method based on wavelet denoising is put forward to accurately predict blood glucose levels. In this method, some auxiliary variables associated with blood glucose are denoised and then used to train the neural network to establish the blood glucose soft-sensing model. The methodology is tested using the simulation data of NO.1 and NO.2 adult. Testing result shows that the blood glucose values obtained by this model has smaller root mean square error, better signal-to-noise ratio, and smaller measurement delay than subcutaneous interstitial fluid glucose values.