Continuous non-invasive blood glucose detection method based on PSO-GRU

Abstract

Aiming at the time-varying, nonlinear and non-stationary problems of continuous non-invasive blood glucose detection data, a Gate Recurrent Unit (GRU) neural network based on particle swarm optimization (PSO) is proposed. Firstly, the electro-optic method was used to extract the Photoplethysmography (PPG) signal of the human body under red and infrared light, and the relevant characteristic parameters were extracted based on the physiological signal characteristics and the vascular elastic cavity model. Finally, a continuous non-invasive blood glucose detection model based on GRU was established, and then the parameters of GRU neural network were optimized by the PSO algorithm with strong optimization ability, which effectively improved the accuracy of the blood glucose detection model. Experiments have verified that GRU optimized by PSO has better accuracy and stability than the traditional neural network, and its accuracy reaches 89.3%.