Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data

被引：5

作者：

Shao, Jian ^{[1
]}

Liu, Ziqing ^{[1
]}

Li, Shaoyun ^{[2
]}

Wu, Benrui ^{[3
]}

Nie, Zedong ^{[4
]}

Li, Yuefei ^{[2
]}

Zhou, Kaixin ^{[1
,5
]}

机构：

[1] Univ Chinese Acad Sci, Dept Life Sci, Beijing, Peoples R China

[2] Chongqing Fifth Peoples Hosp, Chongqing, Peoples R China

[3] Chinese Acad Sci, Inst Biophys, Beijing, Peoples R China

[4] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China

[5] Univ Chinese Acad Sci, Dept Life Sci, Beijing 100049, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL BIOLOGY | 2023年 / 30卷 / 01期

关键词：

CGM data visualization; CGM metrics; continuous glucose monitoring; quality control; time series analysis;

D O I：

10.1089/cmb.2022.0100

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.

引用

页码：112 / 116

页数：5

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