Noninvasive glucose monitoring: increasing accuracy by combination of multi-technology and multi-sensors

Abstract

Background: The main concern in noninvasive (NI) glucose monitoring methods is to achieve high accuracy results despite the fact that no direct blood or interstitial fluid glucose measurement is performed. An alternative approach to increase the accuracy of NI glucose measurement was previously suggested through a combination of three NI methods: ultrasonic, electromagnetic, and thermal. This paper provides further explanation about the nature of the implemented technologies, and multi-sensors are presented, as well as a detailed elaboration on the novel algorithm for data analysis.

Methods: Clinical trials were performed on two different days. During the first day, calibration and six subsequent measurements were performed. During the second day, a "full day" session of about 10 hours took place. During the trial, type 1 and 2 diabetes patients were calibrated and evaluated with GlucoTrack glucose monitor against HemoCue (Glucose 201+).

Results: A total of 91 subjects were tested during the trial period. Clarke error grid (CEG) analysis shows 96% of the readings (on both days 1 and 2) fall in the clinically accepted A and B zones, of which 60% are within zone A. The absolute relative differences (ARDs) yield mean and median values of 22.4% and 15.9%, respectively. The CEG for day 2 of the trial shows 96% of the points in zones A and B, with 57% of the values in zone A. Mean and median ARD values for the readings on day 2 are 23.4% and 16.5%, respectively. The intervals between day 1 (calibration and measurements) and day 2 (measurements only) were 1-22 days, with a median of 6 days.

Conclusions: The presented methodology shows that increased accuracy was indeed achieved by combining multi-technology and multi-sensors. The approach of integration contributes to increasing the signal-to-noise ratio (glucose to other contributors). A combination of several technologies allows compensation of a possible aberration in one modality by the others, while multi-sensor implementation enables corrections for interference contributions. Furthermore, clinical trials indicate the ability of using the device for a wide range of demography, showing clearly that the calibration is valid for long term.

Figure 1.

(A) Main unit with PEC and (B) PEC side view.

Figure 2.

Sensor-tissue mechanical structure.

Figure 3.

Raw process of heating the sensor-tissue structure in subject A (as an example) as measured by the thermal sensor. The different colors of the heating process represent different glucose concentrations.

Figure 4.

Integrated and temperature-corrected equivalent thermal signal in subject A (as an example) versus glucose. The quality of correlation between the heat signal and glucose, shown as R², is 0.63.

Figure 5.

(A) Shematic representation of the earlobe between the two ultrasonic piezo elements. (B) Phase shift between the transmitted and received waves, measured as Δφ.

Figure 6.

Amplified phase shift versus input transducer frequency in the low-frequency region. The amplified phase-shift values are viewed at a chosen frequency, which was found to be the optimal frequency during calibration for subject A. Different colors apply to different glucose levels.

Figure 7.

Amplified phase shift (measured at a chosen frequency) corrected for temperature for subject A (as an example) versus glucose. The quality of corelation between glucose and phase shift after temperature correction, shown as R², is 0.68.

Figure 8.

Electromagnetic channel, where R_in is the input resistance, Z(D,ε) is the transfer operator of the sensing element; f-meter is the auto-oscillation frequency (f) measuring circuit, T is the relay element with hysteresis creating a positive feedback in the auto-oscillating circuit, $D=\frac{\text{d}}{\text{d}t},$ E_s is the electrical potential on the skin surface, and C_p is the parasitic capacitance. The EMC integrator includes the earlobe tissue in the feedback, and the transfer operator time constants depend on the tissue electric permittivity, denoted ε.

Figure 9.

Electromagntic signal (frequency) corrected for temperature for subject A (as an example) versus glucose. The quality of linear correlation between glucose and temperature-corrected electromagnetic signal, shown as R², is 0.81.

Figure 10.

Proof of concept of using combined technologies: (A) raw glucose readings per each technology [(), thermal; (), electromagnetic; (), ultrasonic] and (B) final combined glucose result.

Figure 11.

Build up of a calibration model. US, ultrasonic channel; EM, electromagnetic channel; TH, thermal channel.

Figure 12.

(A) Measurement procedure and (B) schematics of glucose calculation algorithm. US, ultrasonic channel output signal; EM, electromagnetic channel output signal; TH, thermal channel output signal.

Figure 13.

Measurements in day 1.

Figure 14.

Measurement process during day 2.

Figure 15.

(A) Clarke error grid of GlucoTrack glucose monitor readings of all data pairs (1,772 points) for days 1 and 2: 60% in zone A,36% in zone B, 2% in zone C, and 2% in zone D. (B) Clarke error grid of GlucoTrack glucose monitor readings on a different day than calibration (day 2) only (1,225 points): 57% in zone A, 39% in zone B, 2% in zone C, and 2% in zone D.