Andreas Caduff1, Mattia Zanon2, Martin Mueller2, Pavel Zakharov2, Yuri Feldman3, Oscar De Feo2, Marc Donath4, Werner A Stahel5, Mark S Talary2. 1. Biovotion AG, Zurich, Switzerland andreas.caduff@biovotion.com. 2. Biovotion AG, Zurich, Switzerland. 3. Department of Applied Physics, Hebrew University of Jerusalem, Jerusalem, Israel. 4. Clinic for Endocrinology and Diabetes, University Hospital Basel, Basel, Switzerland. 5. Seminar for Statistics, ETH Zurich, Zurich, Switzerland.
Abstract
BACKGROUND: We study here the influence of different patients and the influence of different devices with the same patients on the signals and modeling of data from measurements from a noninvasive Multisensor glucose monitoring system in patients with type 1 diabetes. The Multisensor includes several sensors for biophysical monitoring of skin and underlying tissue integrated on a single substrate. METHOD: Two Multisensors were worn simultaneously, 1 on the upper left and 1 on the upper right arm by 4 patients during 16 study visits. Glucose was administered orally to induce 2 consecutive hyperglycemic excursions. For the analysis, global (valid for a population of patients), personal (tailored to a specific patient), and device-specific multiple linear regression models were derived. RESULTS: We find that adjustments of the model to the patients improves the performance of the glucose estimation with an MARD of 17.8% for personalized model versus a MARD of 21.1% for the global model. At the same time the effect of the measurement side is negligible. The device can equally well measure on the left or right arm. We also see that devices are equal in the linear modeling. Thus hardware calibration of the sensors is seen to be sufficient to eliminate interdevice differences in the measured signals. CONCLUSIONS: We demonstrate that the hardware of the 2 devices worn on the left and right arms are consistent yielding similar measured signals and thus glucose estimation results with a global model. The 2 devices also return similar values of glucose errors. These errors are mainly due to nonstationarities in the measured signals that are not solved by the linear model, thus suggesting for more sophisticated modeling approaches.
BACKGROUND: We study here the influence of different patients and the influence of different devices with the same patients on the signals and modeling of data from measurements from a noninvasive Multisensor glucose monitoring system in patients with type 1 diabetes. The Multisensor includes several sensors for biophysical monitoring of skin and underlying tissue integrated on a single substrate. METHOD: Two Multisensors were worn simultaneously, 1 on the upper left and 1 on the upper right arm by 4 patients during 16 study visits. Glucose was administered orally to induce 2 consecutive hyperglycemic excursions. For the analysis, global (valid for a population of patients), personal (tailored to a specific patient), and device-specific multiple linear regression models were derived. RESULTS: We find that adjustments of the model to the patients improves the performance of the glucose estimation with an MARD of 17.8% for personalized model versus a MARD of 21.1% for the global model. At the same time the effect of the measurement side is negligible. The device can equally well measure on the left or right arm. We also see that devices are equal in the linear modeling. Thus hardware calibration of the sensors is seen to be sufficient to eliminate interdevice differences in the measured signals. CONCLUSIONS: We demonstrate that the hardware of the 2 devices worn on the left and right arms are consistent yielding similar measured signals and thus glucose estimation results with a global model. The 2 devices also return similar values of glucose errors. These errors are mainly due to nonstationarities in the measured signals that are not solved by the linear model, thus suggesting for more sophisticated modeling approaches.
Authors: J Latreille; C Guinot; C Robert-Granié; I Le Fur; M Tenenhaus; J-L Foulley Journal: Skin Pharmacol Physiol Date: 2004 May-Jun Impact factor: 3.479
Authors: Mattia Zanon; Giovanni Sparacino; Andrea Facchinetti; Michela Riz; Mark S Talary; Roland E Suri; Andreas Caduff; Claudio Cobelli Journal: Med Biol Eng Comput Date: 2012-06-22 Impact factor: 2.602
Authors: Miguel A Pleitez; Tobias Lieblein; Alexander Bauer; Otto Hertzberg; Hermann von Lilienfeld-Toal; Werner Mäntele Journal: Rev Sci Instrum Date: 2013-08 Impact factor: 1.523
Authors: Mattia Zanon; Martin Mueller; Pavel Zakharov; Mark S Talary; Marc Donath; Werner A Stahel; Andreas Caduff Journal: J Diabetes Sci Technol Date: 2017-11-16
Authors: Andreas Caduff; Mattia Zanon; Pavel Zakharov; Martin Mueller; Mark Talary; Achim Krebs; Werner A Stahel; Marc Donath Journal: J Diabetes Sci Technol Date: 2018-01-14