Shao Jian, Liu Ziqing, Li Shaoyun, Wu Benrui, Nie Zedong, Li Yuefei, Zhou Kaixin
Department of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
Chongqing Fifth People's Hospital, Chongqing, China.
J Comput Biol. 2023 Jan;30(1):112-116. doi: 10.1089/cmb.2022.0100. Epub 2022 Aug 8.
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.
R包“连续血糖监测时间序列数据分析（CGMTSA）”的开发旨在推动将连续血糖监测（CGM）数据作为时间序列进行研究。因此，引入了新颖的时间序列函数来：（1）实现更准确的缺失数据插补和异常值识别；（2）计算推荐的CGM指标以及关键时间序列参数；（3）绘制交互式三维图形，以便直接可视化CGM时间数据和时间序列模型优化。该软件旨在兼容所有流行的CGM设备，并支持所有常见的数据处理步骤。该程序可在GitHub上获取，适用于Linux、Windows和Mac系统。
