Department of Information Engineering, University of Padova, Padova, Italy.
IEEE Trans Biomed Eng. 2010 Mar;57(3):634-41. doi: 10.1109/TBME.2009.2033264. Epub 2009 Oct 9.
Continuous glucose monitoring (CGM) devices can be very useful in diabetes management. Unfortunately, their use in online applications, e.g., for hypo/hyperalert generation, is made difficult by random noise measurement. Remarkably, the SNR of CGM data varies with the sensor and with the individual. As a consequence, approaches in which filter parameters are not allowed to adapt to the current SNR are likely to be suboptimal. In this paper, we present a new online methodology to reduce noise in CGM signals by a Kalman filter (KF), whose unknown parameters are adjusted in a given individual by a stochastically based smoothing criterion exploiting data of a burn-in interval. The performance of the new KF approach is quantitatively assessed on Monte Carlo simulations and 24 real CGM datasets. Our results are compared with those obtained by a moving-average (MA) filtering approach with fixed parameters currently in use in likely all commercial CGM devices. Results show that the new KF approach performs much better than MA. For instance, on real data, for comparable signal denoising, the delay introduced by KF is about 35% less than that obtained by MA.
连续血糖监测 (CGM) 设备在糖尿病管理中非常有用。然而，由于随机噪声测量，它们在在线应用程序中的使用（例如，用于生成低血糖/高血糖警报）变得困难。值得注意的是，CGM 数据的信噪比随传感器和个体而变化。因此，不允许滤波器参数适应当前信噪比的方法可能不是最优的。在本文中，我们提出了一种新的在线方法，通过卡尔曼滤波器 (KF) 来降低 CGM 信号中的噪声，其未知参数通过利用预热间隔数据的基于随机的平滑准则在给定个体中进行调整。新的 KF 方法的性能在蒙特卡罗模拟和 24 个真实 CGM 数据集上进行了定量评估。我们的结果与目前所有商业 CGM 设备中使用的具有固定参数的移动平均 (MA) 滤波方法的结果进行了比较。结果表明，新的 KF 方法的性能明显优于 MA。例如，在真实数据上，对于可比的信号去噪，KF 引入的延迟比 MA 少约 35%。
