title: The Effect of a Global, Subject, and Device-Specific Model on a Noninvasive Glucose Monitoring Multisensor System.
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.
  
  
  METHODS
  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.affiliation: Biovotion AG, Zurich, Switzerland andreas.caduff@biovotion.com.
authors.forename: Andreas
authors.initials: A
authors.lastname: Caduff
authors.affiliation: Biovotion AG, Zurich, Switzerland.
authors.forename: Mattia
authors.initials: M
authors.lastname: Zanon
authors.affiliation: Biovotion AG, Zurich, Switzerland.
authors.forename: Martin
authors.initials: M
authors.lastname: Mueller
authors.affiliation: Biovotion AG, Zurich, Switzerland.
authors.forename: Pavel
authors.initials: P
authors.lastname: Zakharov
authors.affiliation: Department of Applied Physics, Hebrew University of Jerusalem, Jerusalem, Israel.
authors.forename: Yuri
authors.initials: Y
authors.lastname: Feldman
authors.affiliation: Biovotion AG, Zurich, Switzerland.
authors.forename: Oscar
authors.initials: O
authors.lastname: De Feo
authors.affiliation: Clinic for Endocrinology and Diabetes, University Hospital Basel, Basel, Switzerland.
authors.forename: Marc
authors.initials: M
authors.lastname: Donath
authors.affiliation: Seminar for Statistics, ETH Zurich, Zurich, Switzerland.
authors.forename: Werner A
authors.initials: WA
authors.lastname: Stahel
authors.affiliation: Biovotion AG, Zurich, Switzerland.
authors.forename: Mark S
authors.initials: MS
authors.lastname: Talary
mesh_terms: D000328:Adult; D000465:Algorithms; D001132:Arm / Q000502:physiology; D001703:Biophysics; D001786:Blood Glucose / Q000032:analysis; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation* / Q000379:methods*; D015992:Body Mass Index; D002138:Calibration; D003922:Diabetes Mellitus, Type 1 / Q000097:blood*; D004867:Equipment Design; D006801:Humans; D016014:Linear Models; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000379:methods; D015203:Reproducibility of Results; D012867:Skin / Q000737:chemistry; D012879:Skin Physiological Phenomena
chemical_list: D001786:Blood Glucose
keywords: algorithm; diabetes; electromagnetic; optical; perturbations; wearable device