In vivo noninvasive blood glucose detection using near-infrared spectrum based on the PSO-2ANN model

BACKGROUND: To improving the nursing level of diabetics, it is necessary to develop noninvasive blood glucose method. OBJECTIVE: In order to reduce the number of the near-infrared signal, consider the nonlinear relationship between the blood glucose concentration and near-infrared signal, and correct the individual difference and physiological glucose dynamic, 2 artificial neural networks (2ANN) combined with particle swarm optimization (PSO), named as PSO-2ANN, is proposed. METHOD: Two artificial neural networks (ANNs) are employed as the basic structure of the PSO-ANN model, and the weight coefficients of the two ANNs which represent the difference of individual and daily physiological rule are optimized by particle swarm optimization (PSO). RESULTS: Clarke error grid shows the blood glucose predictions are distributed in regions A and B, Bland-Altman analysis show that the predictions and measurements are in good agreement. CONCLUSIONS: The PSO-2ANN model is a nonlinear calibration strategy with accuracy and robustness using 1550-nm spectroscopy, which can correct the individual difference and physiological glucose dynamics.