Prof. Xiaohui Chen Profile

Prof. Xiaohui Chen

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Proceedings Article | 1 August 2022 Paper

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

Shuangyu Li, Xiaohui Chen

Proceedings Volume 12257, 122571F (2022) https://doi.org/10.1117/12.2640183

KEYWORDS: Blood, Glucose, Neural networks, Particles, Autoregressive models, Particle swarm optimization, Signal detection

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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%.

Proceedings Article | 1 August 2022 Paper

Non-invasive blood viscosity detection method based on random forest

Wen Zhao, Xiaohui Chen

Proceedings Volume 12257, 122571V (2022) https://doi.org/10.1117/12.2640190

KEYWORDS: Blood, Feature extraction, Signal detection, Signal processing, Neurons, Evolutionary algorithms, Photoplethysmography, Neural networks

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In view of the characteristics of the current non-invasive blood viscosity calculation that is complex and easy to be interfered, resulting in low accuracy of calculation results,to further improve the accuracy of blood viscosity prediction,in this paper, the machine learning algorithms of extreme learning machine and random forest are used to accurately measure blood viscosity.First of all, we need to extract the PPG signals of different people and preprocess the PPG signal waveforms to obtain high-quality PPG waveforms. At the same time, the pressure pulse wave signals are collected and preprocessed.Secondly, extract the feature points of the two waveforms and calculate the pulse wave feature parameters according to the extracted feature points.The blood viscosity value is preliminarily estimated according to the characteristic parameters, and the value and other human parameters are used as the input parameters of the prediction model of blood viscosity,through extensive training of parameters, the best prediction model is selected, thereby improving the prediction accuracy of blood viscosity.The experimental results show that the predicted value of blood viscosity obtained by the random forest algorithm is better than that obtained by the extreme learning machine algorithm, and the accuracy reaches 88.4%.

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