BACKGROUND: Modeling the various error components affecting continuous glucose monitoring (CGM) sensors is very important (e.g., to generate realistic scenarios for developing and testing CGM-based applications in type 1 diabetes simulators). Recent work has focused on some error components (i.e., blood-to-interstitium delay, calibration, and random noise), but key events such as transient faults have not been investigated in depth. We propose two mathematical models that describe the disconnections and compression artifacts. MATERIALS AND METHODS: A dataset of 72 subjects monitored with the Dexcom (San Diego, CA) G4(®) Platinum sensor is considered. Disconnections and compression artifacts have been isolated, and some basic statistical parameters (e.g., frequency and duration) have been extracted. A Markov chain model is proposed to describe the dynamics of a disconnection, and the effect of a compression artifact in the CGM profile is modeled as the output of a first-order linear dynamic system driven by a rectangular function. RESULTS: The great majority of disconnections (approximately 90%) lasted less than 20 min. Compression artifact median (5(th)-95(th) percentiles) values were 45 (30-70) min for the duration and 24 (10-48) mg/dL for the amplitude. Both disconnections and compression artifacts happened with almost equal probability during the 7 days of monitoring. Disconnections were more frequent during the day and compression artifacts during the night. A three-state Markov model is shown to be effective to describe the single disconnection. The asymmetric shape of compression artifact is well fitted by the proposed model. CONCLUSIONS: The provided models are sufficiently accurate for simulation purposes (e.g., to create more challenging and realistic scenarios) to test real-time fault detection algorithms and artificial pancreas closed-loop controllers.
BACKGROUND: Modeling the various error components affecting continuous glucose monitoring (CGM) sensors is very important (e.g., to generate realistic scenarios for developing and testing CGM-based applications in type 1 diabetes simulators). Recent work has focused on some error components (i.e., blood-to-interstitium delay, calibration, and random noise), but key events such as transient faults have not been investigated in depth. We propose two mathematical models that describe the disconnections and compression artifacts. MATERIALS AND METHODS: A dataset of 72 subjects monitored with the Dexcom (San Diego, CA) G4(®) Platinum sensor is considered. Disconnections and compression artifacts have been isolated, and some basic statistical parameters (e.g., frequency and duration) have been extracted. A Markov chain model is proposed to describe the dynamics of a disconnection, and the effect of a compression artifact in the CGM profile is modeled as the output of a first-order linear dynamic system driven by a rectangular function. RESULTS: The great majority of disconnections (approximately 90%) lasted less than 20 min. Compression artifact median (5(th)-95(th) percentiles) values were 45 (30-70) min for the duration and 24 (10-48) mg/dL for the amplitude. Both disconnections and compression artifacts happened with almost equal probability during the 7 days of monitoring. Disconnections were more frequent during the day and compression artifacts during the night. A three-state Markov model is shown to be effective to describe the single disconnection. The asymmetric shape of compression artifact is well fitted by the proposed model. CONCLUSIONS: The provided models are sufficiently accurate for simulation purposes (e.g., to create more challenging and realistic scenarios) to test real-time fault detection algorithms and artificial pancreas closed-loop controllers.
Authors: Jianyuan Feng; Kamuran Turksoy; Sediqeh Samadi; Iman Hajizadeh; Elizabeth Littlejohn; Ali Cinar Journal: J Process Control Date: 2017-05-18 Impact factor: 3.666
Authors: Marco Infante; David A Baidal; Michael R Rickels; Andrea Fabbri; Jay S Skyler; Rodolfo Alejandro; Camillo Ricordi Journal: Artif Organs Date: 2021-07-15 Impact factor: 2.663
Authors: J Geoffrey Chase; Jean-Charles Preiser; Jennifer L Dickson; Antoine Pironet; Yeong Shiong Chiew; Christopher G Pretty; Geoffrey M Shaw; Balazs Benyo; Knut Moeller; Soroush Safaei; Merryn Tawhai; Peter Hunter; Thomas Desaive Journal: Biomed Eng Online Date: 2018-02-20 Impact factor: 2.819