The compensation of perturbing temperature fluctuation in glucose monitoring technologies based on impedance spectroscopy.

Non-invasive glucose monitoring techniques based on impedance spectroscopy are affected by a variety of perturbing effects. In order to use the impedance as a glucose measure, these perturbing effects need to be quantified and compensated. Since effects induced by temperature fluctuations certainly rank among the severest perturbations, a clinical study was carried out to establish whether temperature, as a perturbing factor, could be compensated for in impedance spectroscopy. The results as well as a concept allowing for the compensation of perturbing temperature fluctuations are presented here. The compensation technique described is a generic approach that, in principle, can be applied to compensate most perturbation effects provided that there are now multiplicative interactions between the variable of interest (in our case the glucose) and the perturbations. The results allow for the determination of the minimum required sensitivity of an impedance spectrometer to glucose in order to be operational in home-use conditions. Furthermore, the data can be used to estimate if a universal temperature compensation can be applied or if an individual calibration is necessary. For instance, applying a universal temperature compensation and requiring an application range of +/-5 degrees C, the minimum required sensitivity of the minimum impedance and frequency in a sensor-skin RLC circuit to resolve glucose variations equivalent to 10 mg/dl is 0.85 Omega/mg/dl and 0.14 MHz/mg/dl, respectively. The sensitivity requirements reduce by about a factor 1.6, if for each subject an individual calibration is carried out. Depending on the measure and the calibration procedure, the required sensitivities are a factor 3-50 greater than those reported in the literature. Thus, in order to be operational in home-use conditions, the signal-to-noise ratio (S/N) of existing impedance-based monitoring platforms using RLC circuits need to be improved by about one order of magnitude. In order to make non-invasive glucose monitoring possible we, therefore, suggest some measures that may improve the S/N by the required factor.

Characteristics of a multisensor system for non invasive glucose monitoring with external validation and prospective evaluation.

The Multisensor Glucose Monitoring System (MGMS) features non invasive sensors for dielectric characterisation of the skin and underlying tissue in a wide frequency range (1 kHz-100 MHz, 1 and 2 GHz) as well as optical characterisation. In this paper we describe the results of using an MGMS in a miniaturised housing with fully integrated sensors and battery. Six patients with Type I Diabetes Mellitus (age 44±16 y; BMI 24.1±1.3 kg/m(2), duration of diabetes 27±12 y; HbA1c 7.3±1.0%) wore a single Multisensor at the upper arm position and performed a total of 45 in-clinic study days with 7 study days per patient on average (min. 5 and max. 10). Glucose changes were induced either orally or by i.v. glucose administration and the blood glucose was measured routinely. Several prospective data evaluation routines were applied to evaluate the data. The results are shown using one of the restrictive data evaluation routines, where measurements from the first 22 study days were used to train a linear regression model. The global model was then prospectively applied to the data of the remaining 23 study days to allow for an external validation of glucose prediction. The model application yielded a Mean Absolute Relative Difference of 40.8%, a Mean Absolute Difference of 51.9 mg dL(-1), and a correlation of 0.84 on average per study day. The Clarke error grid analyses showed 89.0% in A+B, 4.5% in C, 4.6% in D and 1.9% in the E region. Prospective application of a global, purely statistical model, demonstrates that glucose variations can be tracked non invasively by the MGMS in most cases under these conditions.

Non-invasive glucose monitoring in patients with Type 1 diabetes: a Multisensor system combining sensors for dielectric and optical characterisation of skin.

In vivo variations of blood glucose (BG) are affecting the biophysical characteristics (e.g. dielectric and optical) of skin and underlying tissue (SAUT) at various frequencies. However, the skin impedance spectra for instance can also be affected by other factors, perturbing the glucose related information, factors such as temperature, skin moisture and sweat, blood perfusion as well as body movements affecting the sensor-skin contact. In order to be able to correct for such perturbing factors, a Multisensor system was developed including sensors to measure the identified factors. To evaluate the quality of glucose monitoring, the Multisensor was applied in 10 patients with Type 1 diabetes. Glucose was administered orally to induce hyperglycaemic excursions at two different study visits. For analysis of the sensor signals, a global multiple linear regression model was derived. The respective coefficients of the variables were determined from the sensor signals of this first study visit (R(2)=0.74, MARD=18.0%--mean absolute relative difference). The identical set of modelling coefficients of the first study visit was re-applied to the test data of the second study visit to evaluate the predictive power of the model (R(2)=0.68, MARD=27.3%). It appears as if the Multisensor together with the global linear regression model applied, allows for tracking glucose changes non-invasively in patients with diabetes without requiring new model coefficients for each visit. Confirmation of these findings in a larger study group and under less experimentally controlled conditions is required for understanding whether a global parameterisation routine is feasible.

Validation of human skin models in the MHz region.

The human skin consists of several layers with distinct dielectric properties. Resolving the impact of changes in dielectric parameters of skin layers and predicting them allows for non-invasive sensing in medical diagnosis. So far no complete skin and underlying tissue model is available for this purpose in the MHz range. Focusing on this dispersiondominated frequency region multilayer skin models are investigated: First, containing homogeneous non-dispersive sublayers and second, with sublayers obtained from a three-phase Maxwell-Garnett mixture of shelled cell-like ellipsoids. Both models are numerically simulated using the Finite Element Method, a fringing field sensor on the top of the multilayer system serving as a probe. Furthermore, measurements with the sensor probing skin in vivo are performed. In order to validate the models the uppermost skin layer, the stratum corneum was i) included and ii) removed in models and measurements. It is found that only the Maxwell-Garnett mixture model can qualitatively reproduce the measured dispersion which still occurs without the stratum corneum and consequently, structural features of tissue have to be part of the model.