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1 August 2022 Continuous non-invasive blood glucose detection method based on PSO-GRU
Shuangyu Li, Xiaohui Chen
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Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 122571F (2022) https://doi.org/10.1117/12.2640183
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China
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%.
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Shuangyu Li and Xiaohui Chen "Continuous non-invasive blood glucose detection method based on PSO-GRU", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 122571F (1 August 2022); https://doi.org/10.1117/12.2640183
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KEYWORDS
Blood
Glucose
Neural networks
Autoregressive models
Particles
Particle swarm optimization
Signal detection
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