{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T10:15:48Z","timestamp":1784628948645,"version":"3.55.0"},"reference-count":18,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T00:00:00Z","timestamp":1622332800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:p>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\u2009hours of synthetic CGM measurements. These data have been made available in a public database, which can be used as a reference in future studies.<\/jats:p>","DOI":"10.1177\/19322968211014255","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T02:31:53Z","timestamp":1622428313000},"page":"1220-1223","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["A Conditional Generative Adversarial Network for Synthesis of Continuous Glucose Monitoring Signals"],"prefix":"10.1177","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3484-7571","authenticated-orcid":false,"given":"Simon Lebech","family":"Cichosz","sequence":"first","affiliation":[{"name":"Department of Health Science and Technology, Aalborg University, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5798-2672","authenticated-orcid":false,"given":"Alexander Arndt Pasgaard","family":"Xylander","sequence":"additional","affiliation":[{"name":"Department of Health Science and Technology, Aalborg University, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,5,30]]},"reference":[{"key":"bibr1-19322968211014255","doi-asserted-by":"publisher","DOI":"10.2337\/dc16-2482"},{"key":"bibr2-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1177\/193229681100500320"},{"key":"bibr3-19322968211014255","doi-asserted-by":"publisher","DOI":"10.2337\/dc17-1339"},{"key":"bibr4-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1016\/j.jdiacomp.2017.06.013"},{"key":"bibr5-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1007\/s11892-013-0398-4"},{"key":"bibr6-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.07.007"},{"key":"bibr7-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1177\/1932296814528838"},{"key":"bibr8-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1177\/1932296814549830"},{"key":"bibr9-19322968211014255","doi-asserted-by":"publisher","DOI":"10.3390\/s19194338"},{"key":"bibr10-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2019.0435"},{"key":"bibr11-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1177\/1932296815611680"},{"key":"bibr12-19322968211014255","doi-asserted-by":"crossref","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Accessed November 12, 2020. http:\/\/www.github.com\/goodfeli\/adversarial","DOI":"10.1145\/3422622"},{"key":"bibr13-19322968211014255","doi-asserted-by":"publisher","DOI":"10.2337\/dc15-1426"},{"key":"bibr14-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1210\/jc.2018-02763"},{"key":"bibr15-19322968211014255","doi-asserted-by":"publisher","DOI":"10.17632\/chd8hx65r4.1"},{"key":"bibr16-19322968211014255","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.25.2.275"},{"key":"bibr17-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2005.7.253"},{"key":"bibr18-19322968211014255","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2018.07.010"}],"container-title":["Journal of Diabetes Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968211014255","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/19322968211014255","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968211014255","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T18:15:18Z","timestamp":1777400118000},"score":1,"resource":{"primary":{"URL":"http:\/\/journals.sagepub.com\/doi\/10.1177\/19322968211014255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,30]]},"references-count":18,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["10.1177\/19322968211014255"],"URL":"https:\/\/doi.org\/10.1177\/19322968211014255","relation":{},"ISSN":["1932-2968","1932-2968"],"issn-type":[{"value":"1932-2968","type":"print"},{"value":"1932-2968","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,30]]}}}