Empirical Dynamic Model Identification for Blood-Glucose Dynamics in Response to Physical Activity.
Empirical Dynamic Model Identification for Blood-Glucose Dynamics in Response to Physical Activity.
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DOI：
10.1109/cdc.2015.7402815
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发表时间：
2015-12
期刊：
影响因子：
--
通讯作者：
Dassau E
中科院分区：
文献类型：
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作者：
Dasanayake IS;Seborg DE;Pinsker JE;Doyle FJ 3rd;Dassau E
本文采用子空间辨识方法，捕捉1型糖尿病（T1DM）患者血糖浓度对体力活动的动态响应。采用活动（输入）和皮下血糖测量（输出），通过半定规划构建个性化预测模型。对模型进行校准，随后使用15例T1DM受试者的非重叠数据集进行验证。该初步临床评价揭示了血糖浓度和体力活动之间的潜在线性动力学。这些类型的模型可以增强我们实现更严格的血糖控制和早期检测T1DM患者低血糖的能力。
In this paper, the dynamic response of blood glucose concentration in response to physical activity of people with Type 1 Diabetes Mellitus (T1DM) is captured by subspace identification methods. Activity (input) and subcutaneous blood glucose measurements (output) are employed to construct a personalized prediction model through semi-definite programming. The model is calibrated and subsequently validated with non-overlapping data sets from 15 T1DM subjects. This preliminary clinical evaluation reveals the underlying linear dynamics between blood glucose concentration and physical activity. These types of models can enhance our capabilities of achieving tighter blood glucose control and early detection of hypoglycemia for people with T1DM.