Turksoy Kamuran, Roy Anirban, Cinar Ali
IEEE Trans Biomed Eng. 2017 Jul;64(7):1437-1445. doi: 10.1109/TBME.2016.2535412. Epub 2016 Feb 25.
Faults in subcutaneous glucose concentration readings with a continuous glucose monitoring (CGM) may affect the computation of insulin infusion rates that can lead to hypoglycemia or hyperglycemia in artificial pancreas control systems for patients with type 1 diabetes (T1D).
Multivariable statistical monitoring methods are proposed for detection of faults in glucose concentration values reported by a subcutaneous glucose sensor. A nonlinear first principle glucose/insulin/meal dynamic model is developed. An unscented Kalman filter is used for state and parameter estimation of the nonlinear model. Principal component analysis models are developed and used for detection of dynamic changes. K-nearest neighbor classification algorithm is used for diagnosis of faults. Data from 51 subjects are used to assess the performance of the algorithm.
The results indicate that the proposed algorithm works successfully with 84.2% sensitivity. Overall, 155 (out of 184) of the CGM failures are detected with a 2.8-min average detection time.
A novel algorithm that integrates data-driven and model-based methods is developed. The proposed method is able to detect CGM failures with a high rate of success.
The proposed fault detection algorithm can decrease the effects of faults on insulin infusion rates and reduce the potential for hypo- or hyperglycemia for patients with T1D.
连续血糖监测（CGM）的皮下血糖浓度读数错误可能会影响胰岛素输注速率的计算，进而在1型糖尿病（T1D）患者的人工胰腺控制系统中导致低血糖或高血糖。
提出了多变量统计监测方法，用于检测皮下葡萄糖传感器报告的葡萄糖浓度值中的故障。建立了非线性第一原理葡萄糖/胰岛素/膳食动态模型。采用无迹卡尔曼滤波器对非线性模型进行状态和参数估计。建立主成分分析模型并用于检测动态变化。采用K近邻分类算法进行故障诊断。使用来自51名受试者的数据评估该算法的性能。
结果表明，所提出的算法以84.2%的灵敏度成功运行。总体而言，184次CGM故障中有155次被检测到，平均检测时间为2.8分钟。
开发了一种集成数据驱动和基于模型方法的新算法。所提出的方法能够以高成功率检测CGM故障。
所提出的故障检测算法可以降低故障对胰岛素输注速率的影响，并降低T1D患者发生低血糖或高血糖的可能性。
