Modeling Transient Disconnections and Compression Artifacts of Continuous Glucose Sensors
Modeling Transient Disconnections and Compression Artifacts of Continuous Glucose Sensors
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DOI：
10.1089/dia.2015.0250
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发表时间：
2016-04-01
影响因子：
5.4
通讯作者：
Cobelli, Claudio
中科院分区：
文献类型：
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作者：
Facchinetti, Andrea;Del Favero, Simone;Cobelli, Claudio
背景：对影响连续血糖监测 (CGM) 传感器的各种误差分量进行建模非常重要（例如，生成用于在 1 型糖尿病模拟器中开发和测试基于 CGM 的应用程序的真实场景）。最近的工作集中在一些误差分量（即血液到间质延迟、校准和随机噪声），但尚未深入研究瞬态故障等关键事件。我们提出了两个数学模型来描述断开和压缩伪影。 材料和方法：考虑使用 Dexcom（圣地亚哥，加利福尼亚州）G4((R)) Platinum 传感器监测的 72 名受试者的数据集。断开连接和压缩伪影已被隔离，并且一些基本统计参数（例如频率和持续时间）已被提取。提出了马尔可夫链模型来描述断开的动态，并将 CGM 曲线中压缩伪影的影响建模为由矩形函数驱动的一阶线性动态系统的输出。结果：绝大多数断开（约 90%）持续时间不到 20 分钟。压缩伪影中值（第 5(th)-95(th) 百分位数）值持续时间为 45 (30-70) 分钟，幅度为 24 (10-48) mg/dL。在 7 天的监控期间，断开连接和压缩伪影发生的概率几乎相同。白天断开连接更加频繁，而夜间则出现压缩伪影。三态马尔可夫模型被证明可以有效地描述单次断开。所提出的模型很好地拟合了压缩伪影的不对称形状。结论：所提供的模型对于模拟目的（例如，创建更具挑战性和现实的场景）足够准确，以测试实时故障检测算法和人工胰腺闭环控制器。
Background: Modeling the various error components affecting continuous glucose monitoring (CGM) sensors is very important (e.g., to generate realistic scenarios for developing and testing CGM-based applications in type 1 diabetes simulators). Recent work has focused on some error components (i.e., blood-to-interstitium delay, calibration, and random noise), but key events such as transient faults have not been investigated in depth. We propose two mathematical models that describe the disconnections and compression artifacts.Materials and Methods: A dataset of 72 subjects monitored with the Dexcom (San Diego, CA) G4((R)) Platinum sensor is considered. Disconnections and compression artifacts have been isolated, and some basic statistical parameters (e.g., frequency and duration) have been extracted. A Markov chain model is proposed to describe the dynamics of a disconnection, and the effect of a compression artifact in the CGM profile is modeled as the output of a first-order linear dynamic system driven by a rectangular function.Results: The great majority of disconnections (approximately 90%) lasted less than 20 min. Compression artifact median (5(th)-95(th) percentiles) values were 45 (30-70) min for the duration and 24 (10-48) mg/dL for the amplitude. Both disconnections and compression artifacts happened with almost equal probability during the 7 days of monitoring. Disconnections were more frequent during the day and compression artifacts during the night. A three-state Markov model is shown to be effective to describe the single disconnection. The asymmetric shape of compression artifact is well fitted by the proposed model.Conclusions: The provided models are sufficiently accurate for simulation purposes (e.g., to create more challenging and realistic scenarios) to test real-time fault detection algorithms and artificial pancreas closed-loop controllers.