期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 23, 期 3, 页码 1251-1260出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2018.2840690
关键词
ARIMA model; adaptive orders; blood glucose; hypoglycemia
类别
资金
- National Natural Science Foundation of China [71672006, 71432004, 71501109]
- Fundamental Research Funds for the Central Universities [YWF-18-BJ-J-72]
- Center for Data-Centric Management in the Department of Industrial Engineering at Tsinghua University
- Laboratory of Applied Statistics in the School of Reliability and Systems Engineering at Beihang University
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据