Paper Info
Title
Millimeter-wave Adaptive Glucose Concentration Estimation with Complex-Valued Neural Networks.
Abstract
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.
Year
DOI
Venue
2019
10.1109/TBME.2018.2883085
IEEE transactions on bio-medical engineering
Keywords
Field
DocType
Sugar,Blood,Neurons,Biomedical measurement,Millimeter wave technology,Biological neural networks,Estimation
Magnitude (mathematics),Extremely high frequency,Frequency band,Debye,Computer science,Instability,Penetration depth,Electronic engineering,Ranging,Acoustics,Artificial neural network
Journal
Volume
Issue
ISSN
66
7
1558-2531
Citations 
PageRank 
References 
1
0.34
0
Authors
3
Name
Order
Citations
PageRank
Shizhen Hu110.34
Seko Nagae210.34
Akira Hirose3218.41