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Millimeter-Wave Adaptive Glucose Concentration Estimation With Complex-Valued Neural Networks

In this paper, we propose an adaptive glucose concentration estimation system. The system estimates glucose concentration values non-invasively by making full use of transmission magnitude and phase data. Debye relaxation model indicates that, in millimeter wave frequency range, we can acquire both a high sensitivity and a sufficient penetration depth. Based on the physical model, we choose 60–80 GHz frequency band millimeter wave. We build a single output-neuron complex-valued neural network (CVNN) for adaptive concentration estimation. Glucose water solution samples ranging from 0 to 300 mg/dL are measured. Transmission magnitude and phase data with teacher signals are fed to a CVNN for training and validation. The change in the glucose concentration presents a variation of both transmission magnitude and phase. The CVNN learns the relationship between the transmission data and the glucose concentrations. We find that the system shows a good generalization ability to estimate the concentration for unknown samples. It is effective in the estimation of the glucose concentration in the clinically practical range. Non-invasive methods usually suffer from instability in measurement condition. Our proposed method has the adaptability to different measurement conditions through the learning process based on a set of sample transmission magnitude and phase data with corresponding teacher signals.