Real-Time Hypoglycemia Detection from Continuous Glucose Monitoring Data of Subjects with Type 1 Diabetes
Real-Time Hypoglycemia Detection from Continuous Glucose Monitoring Data of Subjects with Type 1 Diabetes
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DOI：
10.1089/dia.2013.0069
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发表时间：
2013-07-01
影响因子：
5.4
通讯作者：
Hejlesen, Ole Kristian
中科院分区：
文献类型：
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作者：
Jensen, Morten Hasselstrom;Christensen, Toke Folke;Hejlesen, Ole Kristian
背景：低血糖是一种潜在的致命疾病。动态血糖监测（CGM）有可能真实的时间检测低血糖，从而减少低血糖的时间，避免血糖水平进一步下降。然而，CGM是不准确的，并且显示了CGM未检测到低血糖事件的大量病例。本研究的目的是开发一种模式分类模型，以优化实时低血糖detection.Materials和方法：功能，如时间，因为最后一次胰岛素注射和线性回归，峰度和偏度的CGM信号在不同的时间间隔提取的数据，10名男性受试者经历17胰岛素诱导的低血糖事件在实验设置。用SEPCOR和正向选择消除非区分性特征。的功能组合中使用的支持向量机模型和基于样本的灵敏度和特异性和基于事件的灵敏度和假阳性的数量进行评估的性能。结果：最好的模型是由使用7个功能，并能够检测17个低血糖事件与一个假阳性相比，12 17个低血糖事件与零假阳性的CGM单独。前置时间分别为14分钟和0分钟的模型和CGM单独，respectively.Conclusions：这种优化的实时低血糖检测提供了一种独特的方法，为糖尿病患者减少低血糖的时间和了解模式的葡萄糖波动。虽然这些结果是有希望的，该模型需要验证的CGM数据从自发性低血糖事件的患者。
Background: 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.Materials and Methods: 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.Results: 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.Conclusions: 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.