Abstract

The high accurate detection of blood glucose level (BGL) is very important for non-invasive monitoring of diabetes mellitus. In this work, the optoacoustic (OA) quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts of multiple factors (irradiation energy, concentration, temperature, flow rate and vessel depth) was firstly studied. To achieve this aim, a set of OA in vitro detection system of blood glucose with the comprehensive influence of five factors was constructed. The real-time OA signals of 625 rabbit whole blood were obtained at the characteristic wavelength of 750 nm, as well as peak-to-peak values (PPVs). Results show that the accurate detection of BGL was very difficult due to the complicated OA signals. To accurately predict the BGL under the comprehensive impacts of five factors, wavelet neural network (WNN) was employed to train BGL of 500 training set blood. The mean square error (MSE) of BGL for 125 testing set blood was 6.5782 mmol/L. To decrease the MSE, WNN optimized by quantum particle swarm optimization (QPSO), i.e., QPSO-WNN algorithm was utilized. The MSE of BGL based on QPSO-WNN was 0.37485 mmol/L, which was superior to 0.48005 mmol/L of PSO-WNN. Particularly, to further decrease MSE, a novel nonlinear dynamic shrinkage coefficient (DSC) strategy was proposed, and compared with other four kinds of DSC strategies and the fixed one. With the optimal parameters, the MSE of BGL was decreased to 0.3088 mmol/L. Comparison results of seven algorithms and research works demonstrate that OA technology combined with QPSO-WNN algorithm and the novel nonlinear DSC strategy has excellent performance in the quantitative detection of diabetes mellitus involving in the comprehensive impacts.

摘要

血糖水平（BGL）的高精度检测对于糖尿病的无创监测非常重要。本工作首先研究了涉及多因素（照射能量、浓度、温度、流速和血管深度）综合影响的糖尿病光声（OA）体外定量检测。为了达到这个目的，一套 OA in vitro构建了五因素综合影响的血糖检测体系。在 750 nm 的特征波长处获得 625 只兔全血的实时 OA 信号，以及峰峰值 (PPV)。结果表明，由于复杂的OA信号，准确检测BGL非常困难。为了准确预测五种因素综合影响下的BGL，采用小波神经网络（WNN）对500个训练集血液的BGL进行训练。125份测试集血液的BGL均方误差（MSE）为6.5782 mmol/L。为了降低MSE，采用了量子粒子群优化（QPSO）优化的WNN，即QPSO-WNN算法。基于QPSO-WNN的BGL的MSE为0.37485 mmol/L，优于PSO-WNN的0.48005 mmol/L。特别地，为了进一步降低 MSE，提出了一种新颖的非线性动态收缩系数（DSC）策略，并与其他四种DSC策略和固定策略进行了比较。在最佳参数下，BGL 的 MSE 降至 0.3088 mmol/L。七种算法和研究工作的比较结果表明，OA技术结合QPSO-WNN算法和新颖的非线性DSC策略在定量检测涉及综合影响的糖尿病方面具有优异的性能。