IEEE J Biomed Health Inform. 2019 May;23(3):1251-1260. doi: 10.1109/JBHI.2018.2840690. Epub 2018 May 25.
The continuous glucose monitoring system is an effective tool, which enables the users to monitor their blood glucose (BG) levels. Based on the continuous glucose monitoring (CGM) data, we aim at predicting future BG levels so that appropriate actions can be taken in advance to prevent hyperglycemia or hypoglycemia. Due to the time-varying nonstationarity of CGM data, verified by Augmented Dickey-Fuller test and analysis of variance, an autoregressive integrated moving average (ARIMA) model with an adaptive identification algorithm of model orders is proposed in the prediction framework. Such identification algorithm adaptively determines the model orders and simultaneously estimates the corresponding parameters using Akaike Information Criterion and least square estimation. A case study is conducted with the CGM data of diabetics under daily living conditions to analyze the prediction performance of the proposed model together with the early hypoglycemic alarms. Results show that the proposed model outperforms the adaptive univariate model and ARIMA model.
连续血糖监测系统是一种有效的工具，它使用户能够监测自己的血糖（BG）水平。基于连续血糖监测（CGM）数据，我们旨在预测未来的 BG 水平，以便提前采取适当的措施来预防高血糖或低血糖。由于连续血糖监测数据具有时变非平稳性，经增广迪基-富勒检验和方差分析验证，在预测框架中提出了一种具有模型阶自适应识别算法的自回归积分移动平均（ARIMA）模型。该识别算法自适应地确定模型阶数，并使用赤池信息量准则和最小二乘估计同时估计相应的参数。通过对日常条件下糖尿病患者的连续血糖监测数据进行案例研究，分析了所提出的模型与早期低血糖报警的预测性能。结果表明，所提出的模型优于自适应单变量模型和 ARIMA 模型。
