当前位置: X-MOL 学术IEEE J. Biomed. Health Inform. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Pattern recognition reveals characteristic postprandial glucose changes: Non-individualized meal detection in diabetes mellitus type 1
IEEE Journal of Biomedical and Health Informatics ( IF 7.7 ) Pub Date : 2020-02-01 , DOI: 10.1109/jbhi.2019.2908897
Konstanze Kolle , Torben Biester , Sverre Christiansen , Anders Lyngvi Fougner , Oyvind Stavdahl

Accurate continuous glucose monitoring (CGM) is essential for fully automated glucose control in diabetes mellitus type 1. State-of-the-art glucose control systems automatically regulate the basal insulin infusion. Users still need to manually announce meals to dose the prandial insulin boluses. An automated meal detection could release the user and improve the glucose regulation. In this study, patterns in the postprandial CGM data are exploited for meal detection. Binary classifiers are trained to recognize the postprandial pattern in horizons of the estimated glucose rate of appearance and in CGM data. The appearance rate is determined by moving horizon estimation based on a simple model. Linear discriminant analysis (LDA) is used for classification. The proposed method is compared to methods that detect meals when thresholds are violated. Diabetes care data from 12 free-living pediatric patients was downloaded during regular screening. Experts identified meals and their start by retrospective evaluation. The classification was tested by cross-validation. Compared to the threshold-based methods, LDA showed higher sensitivity to meals with a low rate of false alarms. Classifying horizons outperformed the other methods also with respect to time of detection. The onset of meals can be detected by pattern recognition based on estimated model states and consecutive CGM measurements. No individual tuning is necessary. This makes the method easily adopted in the clinical practice.

中文翻译:

模式识别揭示餐后血糖的特征变化:1型糖尿病的非个性化餐食检测

准确的连续葡萄糖监测(CGM)对于1型糖尿病的全自动血糖控制至关重要。先进的血糖控制系统可自动调节基础胰岛素的输注。用户仍然需要手动宣布进餐以对餐前胰岛素大剂量给药。自动进餐检测可以释放使用者并改善血糖调节。在这项研究中,餐后CGM数据中的模式被用于膳食检测。对二元分类器进行训练,以在估计的葡萄糖外观速率和CGM数据中识别餐后模式。通过基于简单模型的移动视界估计来确定出现率。线性判别分析(LDA)用于分类。将所提出的方法与违反阈值时检测膳食的方法进行了比较。在常规筛查期间下载了来自12名自由生活儿科患者的糖尿病护理数据。专家们通过回顾性评估来确定膳食及其开始。通过交叉验证对分类进行测试。与基于阈值的方法相比,LDA对进餐具有更高的敏感性,误报率较低。就检测时间而言,对地平线进行分类的效果也优于其他方法。可以根据估计的模型状态和连续的CGM测量值,通过模式识别来检测进餐。无需单独调整。这使得该方法易于在临床实践中采用。通过交叉验证对分类进行测试。与基于阈值的方法相比,LDA对餐点的敏感性更高,误报率也较低。就检测时间而言,对地平线进行分类的效果也优于其他方法。可以根据估计的模型状态和连续的CGM测量值,通过模式识别来检测进餐。无需单独调整。这使得该方法易于在临床实践中采用。通过交叉验证对分类进行测试。与基于阈值的方法相比,LDA对进餐具有更高的敏感性,误报率较低。就检测时间而言,对地平线进行分类的效果也优于其他方法。可以根据估计的模型状态和连续的CGM测量值,通过模式识别来检测进餐。无需单独调整。这使得该方法易于在临床实践中采用。
更新日期:2020-02-01
down
wechat
bug