Data processing for noninvasive continuous glucose monitoring with a multisensor device

Abstract

Background: Impedance spectroscopy has been shown to be a candidate for noninvasive continuous glucose monitoring in humans. However, in addition to glucose, other factors also have effects on impedance characteristics of the skin and underlying tissue.

Method: Impedance spectra were summarized through a principal component analysis and relevant variables were identified with Akaike's information criterion. In order to model blood glucose, a linear least-squares model was used. A Monte Carlo simulation was applied to examine the effects of personalizing models.

Results: The principal component analysis was able to identify two major effects in the impedance spectra: a blood glucose-related process and an equilibration process related to moisturization of the skin and underlying tissue. With a global linear least-squares model, a coefficient of determination (R²) of 0.60 was achieved, whereas the personalized model reached an R² of 0.71. The Monte Carlo simulation proved a significant advantage of personalized models over global models.

Conclusion: A principal component analysis is useful for extracting glucose-related effects in the impedance spectra of human skin. A linear global model based on Solianis Multisensor data yields a good predictive power for blood glucose estimation. However, a personalized linear model still has greater predictive power.

© 2011 Diabetes Technology Society.

Figures

Figure 1

Two Multisensors attached to the upper arm of a subject with a zoom of the Multisensor substrate holding sensors and electrodes. Fringing field sensors: (A) deep, (B) mid, and (C) shallow penetration of the electromagnetic field; (D) temperature sensor; (E) interdigitated sweat sensor (0.05 mm) for galvanic skin response for superficial sweat monitoring; (F) silicon wafer-based optical reflection sensor with three wavelengths (568/660/880 nm) and two differently shaped photodiodes for skin blood perfusion measurement; (G) humidity sensor, and (H) three-axes acceleration sensor.

Figure 2

Plots with results of all runs (study visits). Time series of the invasively measured BGL (dashed line) and the estimation of the BGL (solid line) of the global model. Letters were used to identify the different patients (A, B, …) and numbers to count the runs (study visits) of patients (A1, A2, …). The time is given in hours from the start of the run.

Figure 3

Proposed BGL profile of a patient during a study visit (run). The approximate profile was realized with a standardized meal and subcutaneous insulin administration.

Figure 4

Time series of (A) bands and (B) principal components of the conductance of the long electrode spectrum for two representative runs (study visits). The level of principal component 1 (solid line) depends on the attachment and placement of the device. Component 1 is related to dielectric changes of the skin tissue triggered by changes in the BGL. Component 2 (dotted line) reflects sweat and skin moisturization effects.

Figure 5

Weights of the first two principal components of the conductance of the long electrode spectrum.

Figure 6

As in Figure 2 but for the personalized model.

Figure 7

Simulated coefficients of determination (R2) of models with individual coefficients for randomly chosen subsets of runs (same sizes as the personal subsets). The vertical line shows the R2 of the personalized model (0.71). For the Monte Carlo simulated models, 1.8% have a higher R2 than the personalized model.