2026-04-17T03:48:51Zhttps://recercat.cat/oai/requestoai:recercat.cat:10256/142672024-06-18T12:17:05Zcom_2072_452955com_2072_2054col_2072_453069
RECERCAT
author
Biagi, Lyvia
author
Ramkissoon, Charrise Mary
author
Facchinetti, Andrea
author
Leal Moncada, Yenny Teresa
author
Vehí, Josep
2024-06-18T12:17:05Z
2024-06-18T12:17:05Z
2017-06-12
http://hdl.handle.net/10256/14267
Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensorThis project has been partially supported by the Spanish Government through Grants DPI-2013-46982-C2-2-R and DPI-2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grant 202050/2015-7, the University of Girona through Grant BR2014/51, and the Agency for Management of University and Research Grants of the Government of Catalonia, Spain (Beatriu de Pinós [BP-DGR 2013])
Attribution 3.0 Spain http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess
Biosensors
Medicina -- Aparells i instruments
Medical instruments and apparatus
Biosensors -- Calibratge
Biosensors -- Calibration
Diabetis -- Tractament
Diabetes -- Treatment
Control intel·ligent
Intelligent control systems
Pàncrees artificial
Artificial pancreas
Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion