Department of Information Engineering, University of Padova, Via Gradenigo 6/B, 35131 Padova, Italy.
Sensors (Basel). 2010;10(7):6751-72. doi: 10.3390/s100706751. Epub 2010 Jul 12.
The availability of continuous glucose monitoring (CGM) sensors allows development of new strategies for the treatment of diabetes. In particular, from an on-line perspective, CGM sensors can become "smart" by providing them with algorithms able to generate alerts when glucose concentration is predicted to exceed the normal range thresholds. To do so, at least four important aspects have to be considered and dealt with on-line. First, the CGM data must be accurately calibrated. Then, CGM data need to be filtered in order to enhance their signal-to-noise ratio (SNR). Thirdly, predictions of future glucose concentration should be generated with suitable modeling methodologies. Finally, generation of alerts should be done by minimizing the risk of detecting false and missing true events. For these four challenges, several techniques, with various degrees of sophistication, have been proposed in the literature and are critically reviewed in this paper.
连续血糖监测（CGM）传感器的出现为糖尿病治疗策略的发展提供了新的可能。特别是从在线角度来看，CGM 传感器可以通过提供能够在预测血糖浓度超过正常范围阈值时发出警报的算法而变得“智能”。为此，至少需要在线考虑和处理四个重要方面。首先，必须对 CGM 数据进行准确校准。然后，需要对 CGM 数据进行滤波，以提高其信噪比（SNR）。第三，需要使用合适的建模方法来生成未来血糖浓度的预测。最后，通过最小化检测虚假和遗漏真实事件的风险来生成警报。针对这四个挑战，文献中提出了多种技术，其复杂程度各不相同，并在本文中进行了批判性回顾。
