Optoacoustic quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts based on improved quantum particle swarm optimized wavelet neural network

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.