Department of Health Science and Technology, Aalborg University, Denmark.
J Diabetes Sci Technol. 2022 Sep;16(5):1220-1223. doi: 10.1177/19322968211014255. Epub 2021 May 30.
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.
本报告描述了如何使用条件生成对抗网络 (CGAN) 从健康个体和不同 HbA1c 水平的 1 型糖尿病个体中合成真实的连续血糖监测系统 (CGM)。结果表明，尽管 CGAN 生成的数据并不完全反映真实的 CGM，但许多重要特征在合成信号中得到了捕捉和反映。简要讨论了异质数据源如何对预测性 CGM 模型的比较构成挑战。因此，通过训练有素的 CGAN 生成了 40,000 天的 CGM 数据，相当于 940,000 小时的合成 CGM 测量。这些数据已经在一个公共数据库中提供，可以在未来的研究中作为参考。
