Blood Glucose Concentration Prediction Based on Double Decomposition and Deep Extreme Learning Machine Optimized by Nonlinear Marine Predator Algorithm

Yang Shen, Deyi Li, Wenbo Wang, Xu Dong

Continuous glucose monitoring data have strong time variability as well as complex non-stationarity and nonlinearity. The existing blood glucose concentration prediction models often overlook the impacts of residual components after multi-scale decomposition on prediction accuracy. To enhance the prediction accuracy, a new short-term glucose prediction model that integrates the double decomposition technique, nonlinear marine predator algorithm (NMPA) and deep extreme learning machine (DELM) is proposed. First of all, the initial blood glucose data are decomposed by variational mode decomposition (VMD) to reduce its complexity and non-stationarity. To make full use of the decomposed residual component, the time-varying filter empirical mode decomposition (TVF-EMD) is utilized to decompose the component, and further realize complete decomposition. Then, the NMPA algorithm is utilized to optimize the weight parameters of the DELM network to avoid any fluctuations in prediction performance, and all the decomposed subsequences are predicted separately. Finally, the output results of each model are superimposed to acquire the predicted value of blood sugar concentration. Using actual collected blood glucose concentration data for predictive analysis, the results of three patients show the following: (i) The double decomposition strategy effectively reduces the complexity and volatility of the original sequence and the residual component. Making full use of the important information implied by the residual component has the best decomposition effect; (ii) The NMPA algorithm optimizes DELM network parameters, which can effectively enhance the predictive capabilities of the network and acquire more precise predictive results; (iii) The model proposed in this paper can achieve a high prediction accuracy of 45 min in advance, and the root mean square error values are 5.2095, 4.241 and 6.3246, respectively. Compared with the other eleven models, it has the best prediction accuracy.