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首页> 外文会议>2018 3rd International Conference on Circuits, Control, Communication and Computing >Noise Reduction in Continuous Blood Glucose Sensor using Physiology based Kalman Filter for Artificial Pancreatic System
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Noise Reduction in Continuous Blood Glucose Sensor using Physiology based Kalman Filter for Artificial Pancreatic System

机译：基于生理的卡尔曼滤波器用于人造胰腺系统的连续血糖传感器的降噪

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摘要

Continuous Glucose Monitoring (CGM) is an essential module of Artificial Pancreatic system. The efficiency of this system in maintaining the blood sugar level is of high significance for glucose control in T1DM patients. often, the sensor data is corrupted by random noises such as zero-mean white Gaussian noise (WGN). Conventional noise rejection techniques such as FIR and IIR digital filter, Moving Average (MA) filter are discussed in the literature. More recently, Kalman Filtering (KF) has been broadly studied for glucose sensor measurement. However, the glucose-insulin (GI) dynamics were modelled as double integrated random walk model rather than a physiological model in noise filter. Moreover, the relation of noise reduction scheme on controller action is not reported in the literature. Therefore, this work proposes a physiology model-based noise filter scheme for glucose sensor noise reduction. Moreover, the effects of noise reduction system on PID controller action is also studied here. The simulation results show that the suggested physiology based KF showed improved performance in noise reduction and also leads to effective glucose control.
机译：连续血糖监测（CGM）是人工胰腺系统的基本模块。该系统维持血糖水平的效率对T1DM患者的血糖控制具有重要意义。通常，传感器数据会受到随机噪声（例如零均值高斯白噪声（WGN））的破坏。文献中讨论了常规的噪声抑制技术，例如FIR和IIR数字滤波器，移动平均（MA）滤波器。最近，对葡萄糖传感器测量进行了广泛的卡尔曼滤波（KF）研究。但是，葡萄糖-胰岛素（GI）动力学建模为双重集成随机游走模型，而不是噪声过滤器中的生理模型。而且，在文献中没有报道降噪方案与控制器动作的关系。因此，这项工作提出了一种基于生理模型的噪声过滤器方案，用于降低葡萄糖传感器的噪声。此外，这里还研究了降噪系统对PID控制器动作的影响。模拟结果表明，建议的基于生理的KF表现出改善的降噪性能，并导致有效的葡萄糖控制。

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