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. 2022 Sep;16(5):1220-1223.
doi: 10.1177/19322968211014255. Epub 2021 May 30.

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

Simon Lebech Cichosz  1 , Alexander Arndt Pasgaard Xylander  1
Affiliations
Free PMC article

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

Simon Lebech Cichosz et al. J Diabetes Sci Technol. 2022 Sep.
Free PMC article

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.

Keywords: CGM; artificial intelligence; generative adversarial networks; type 1 diabetes.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
The architecture of the CGAN model.
Figure 2.
Figure 2.
Examples of 4 synthetic generated CGM signals, one for each of the classes; healthy, diabetes with A1C <7%, diabetes with A1C 7-8%, and diabetes with A1C >8%.
See this image and copyright information in PMC

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