文章基本信息

标题：Fault Detection in Continuous Glucose Monitoring Sensors for Artificial Pancreas Systems
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作者：Xia Yu ; Mudassir Rashid ; Jianyuan Feng 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：18
页码：714-719
DOI：10.1016/j.ifacol.2018.09.279
语种：English
出版社：Elsevier
摘要：AbstractContinuous glucose monitoring (CGM) sensors are a critical component of artificial pancreas (AP) systems that enable individuals with type 1 diabetes to achieve tighter blood glucose control. CGM sensor signals are often afflicted by a variety of anomalies, such as biases, drifts, random noises, and pressure-induced sensor attenuations. To improve the accuracy of CGM measurements, an on-line fault detection method is proposed based on sparse recursive kernel filtering algorithms to identify faults in glucose concentration values. The fault detection algorithm is designed to effectively handle the nonlinearity of the measurements and to differentiate the normal variability in the glycemic dynamics from sensor anomalies. The effectiveness of the proposed recursive kernel filtering algorithm for sensor error detection is demonstrated using simulation studies.
关键词：KeywordsKernel filtering algorithmssparsificationfaults detectionsensor errorsartificial pancreas