Prediction of Adverse Glycemic Events From Continuous Glucose Monitoring Signal
Prediction of Adverse Glycemic Events From Continuous Glucose Monitoring Signal
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DOI：
10.1109/jbhi.2018.2823763
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发表时间：
2019-03-01
影响因子：
7.7
通讯作者：
Rossi, Michele
中科院分区：
文献类型：
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作者：
Gadaleta, Matteo;Facchinetti, Andrea;Rossi, Michele
任何糖尿病治疗的最重要目标是将血糖浓度维持在正常血糖范围内，避免或至少减轻严重的低血糖/高血糖发作。现代连续葡萄糖监测（CGM）设备承诺为患者提供更多和及时的血糖状况意识，因为这些血糖状况危险地接近低/高血糖。挑战是以合理的提前检测导致危险情况的模式，允许患者基于未来（预测的）葡萄糖浓度水平做出治疗决定。我们强调，近年来提出的方法的技术上合理的性能比较尚未完成，因此不清楚哪一个是首选。本研究的目的是通过对最常见的血糖事件预测方法进行比较分析来填补这一空白。回归和分类算法已经实现和分析，包括静态和动态训练方法。该数据集包括89个连续7天在糖尿病受试者中测量的CGM时间序列。性能指标，专门定义为评估和比较的方法的事件预测能力，已被引入和分析。我们的数值结果表明，静态训练方法具有更好的性能，特别是当考虑回归方法时。然而，分类器在针对特定事件类别（例如高血糖症）进行训练时显示出一些改进，实现了与回归器相当的性能，具有更快预测事件的优势。
The most important objective of any diabetes therapy is to maintain the blood glucose concentration within the euglycemic range, avoiding or at least mitigating critical hypo/hyperglycemic episodes. Modern continuous glucose monitoring (CGM) devices bear the promise of providing the patients with an increased and timely awareness of glycemic conditions as these get dangerously near to hypo/hyperglycemia. The challenge is to detect, with reasonable advance, the patterns leading to risky situations, allowing the patient to make therapeutic decisions on the basis of future (predicted) glucose concentration levels. We underline that a technically sound performance comparison of the approaches proposed in recent years has yet to be done, thus it is unclear which one is preferred. The aim of this study is to fill this gap by carrying out a comparative analysis among the most common methods for glucose event prediction. Both regression and classification algorithms have been implemented and analyzed, including static and dynamic training approaches. The dataset consists of 89 CGM time series measured in diabetic subjects for 7 subsequent days. Performance metrics, specifically defined to assess and compare the event-prediction capabilities of the methods, have been introduced and analyzed. Our numerical results show that a static training approach exhibits better performance, in particular when regression methods are considered. However, classifiers show some improvement when trained for a specific event category, such as hyperglycemia, achieving performance comparable to the regressors, with the advantage of predicting the events sooner.