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A Conditional Generative Adversarial Network for Synthesis of Continuous Glucose Monitoring Signals.

Authors
  • Cichosz, Simon Lebech1
  • Xylander, Alexander Arndt Pasgaard1
  • 1 Department of Health Science and Technology, Aalborg University, Denmark. , (Denmark)
Type
Published Article
Journal
Journal of Diabetes Science and Technology
Publisher
SAGE Publications
Publication Date
Sep 01, 2022
Volume
16
Issue
5
Pages
1220–1223
Identifiers
DOI: 10.1177/19322968211014255
PMID: 34056935
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

This report describes how a Conditional Generative Adversarial Network (CGAN) was used to synthesize realistic continuous glucose monitoring systems (CGM) from healthy individuals and individuals with type 1 diabetes over a range of different HbA1c levels. The results showed that even though the CGAN generated data, did not perfectly reflect real world CGM, many of the important features were captured and reflected in the synthetic signals. It is briefly discussed how heterogenous data sources constitutes a challenge for comparison of predictive CGM models. Therefore 40,000 CGM days were generated by the trained CGAN, equivalent to 940,000 hours of synthetic CGM measurements. These data have been made available in a public database, which can be used as a reference in future studies.

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