JOURNAL ARTICLE

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

Jian ShaoZiqing LiuShaoyun LiBenrui WuZedong NieYuefei LiKaixin Zhou

Year: 2022 Journal:   Journal of Computational Biology Vol: 30 (1)Pages: 112-116   Publisher: Mary Ann Liebert, Inc.

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.

Keywords:
Time series Computer science Data mining Outlier Software Imputation (statistics) Series (stratigraphy) Real-time computing Missing data Machine learning Artificial intelligence Operating system

Metrics

7
Cited By
1.47
FWCI (Field Weighted Citation Impact)
20
Refs
0.77
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Diabetes Management and Research
Health Sciences →  Medicine →  Endocrinology, Diabetes and Metabolism
Metabolomics and Mass Spectrometry Studies
Life Sciences →  Biochemistry, Genetics and Molecular Biology →  Molecular Biology
Time Series Analysis and Forecasting
Physical Sciences →  Computer Science →  Signal Processing

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