Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, Aalborg, Denmark.
Diabetes Technol Ther. 2013 Jul;15(7):538-43. doi: 10.1089/dia.2013.0069. Epub 2013 Apr 30.
Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection.
Features such as time since last insulin injection and linear regression, kurtosis, and skewness of the CGM signal in different time intervals were extracted from data of 10 male subjects experiencing 17 insulin-induced hypoglycemic events in an experimental setting. Nondiscriminative features were eliminated with SEPCOR and forward selection. The feature combinations were used in a Support Vector Machine model and the performance assessed by sample-based sensitivity and specificity and event-based sensitivity and number of false-positives.
The best model was composed by using seven features and was able to detect 17 of 17 hypoglycemic events with one false-positive compared with 12 of 17 hypoglycemic events with zero false-positives for the CGM alone. Lead-time was 14 min and 0 min for the model and the CGM alone, respectively.
This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events.
低血糖是一种潜在的致命病症。连续血糖监测（CGM）有可能实时检测低血糖，从而减少低血糖时间并避免血糖水平进一步下降。然而，CGM 并不准确，会出现大量未能被 CGM 检测到的低血糖事件。本研究旨在开发一种模式分类模型，以优化实时低血糖检测。
从 10 名男性受试者在实验环境中经历的 17 次胰岛素诱导的低血糖事件的数据中提取了时间自上次胰岛素注射、CGM 信号的线性回归、峰度和偏度等特征。使用 SEPCOR 和前向选择消除无判别力的特征。将特征组合用于支持向量机模型中，并通过基于样本的敏感性和特异性以及基于事件的敏感性和假阳性数量来评估性能。
最佳模型由七个特征组成，与单独使用 CGM 检测到 17 次低血糖事件中的 12 次相比，该模型能够检测到 17 次低血糖事件中的 17 次，且仅有一次假阳性。模型和 CGM 单独的前置时间分别为 14 分钟和 0 分钟。
这种优化的实时低血糖检测为糖尿病患者提供了一种独特的方法，可以减少低血糖时间并了解血糖波动的模式。尽管这些结果很有希望，但该模型仍需要在患有自发性低血糖事件的患者的 CGM 数据上进行验证。
