Blood Glucose Prediction With VMD and LSTM Optimized by Improved Particle Swarm Optimization

The time series of blood glucose concentration in diabetics are time-varying, nonlinear and non-stationary. To improve the accuracy of blood glucose prediction, a short-term blood glucose prediction model (VMD-IPSO-LSTM) combining variational modal decomposition (VDM) and improved Particle swarm optimization optimizing Long short-term memory network (IPSO-LSTM) was proposed. Firstly, the time series of blood glucose concentration of patients was decomposed by using VMD method to obtain the intrinsic modal functions (IMF) of blood glucose components in different frequency bands, so as to reduce the non-stationarity of blood glucose time series. Then a prediction model was established for each blood glucose component IMF by using the long and short time memory network. Since the number of neurons, learning rate and time window length of LSTM are difficult to determine, the improved PSO algorithm is used to optimize these parameters. The optimized LSTM network was used to predict each IMF, and finally the predicted subsequence was superimposed to obtain the final prediction result. The data of 56 patients were selected as experimental data from 451 patients with diabetes mellitus. The experimental results showed that the proposed VMD-IPSO-LSTM model could achieve high prediction accuracy at 30min, 45min and 60min in advance. When predicted 60 minutes in advance, compared with the LSTM, VMD-LSTM and VMD-PSO-LSTM methods, the RMSE of proposed method decreased by 15.565,3.402,1.215 and the MAPE of proposed method decreased by 11.284%,2.024%, 0.834%, and the percentage of predicted values falling into the A zone increased by 23.5%,6.1% and 2.8% in the Clarke error grid, respectively. The improved accuracy of blood glucose prediction and longer prediction time can provide sufficient time for physicians and patients to control blood glucose concentrations and improve the effectiveness of diabetes treatment.