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

Authors :
Shao, Jian
Liu, Ziqing
Li, Shaoyun
Wu, Benrui
Nie, Zedong
Li, Yuefei
Zhou, Kaixin
Source :
Journal of Computational Biology. jan2023, Vol. 30 Issue 1, p112-116. 5p.
Publication Year :
2023

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10665277
Volume :
30
Issue :
1
Database :
Academic Search Index
Journal :
Journal of Computational Biology
Publication Type :
Academic Journal
Accession number :
161178043
Full Text :
https://doi.org/10.1089/cmb.2022.0100