Non-Invasive Glucose Measurement Using Sub-Terahertz Sensor, Time Domain Processing, and Neural Network

This paper reports a non-invasive sub-Terahertz glucose concentration measurement system consisting of Sensor Unit (SU) and Processing Unit (PU). The SU. uses waveguide probe sensors to obtain the S parameters of glucose samples of different concentrations. These S parameters depend on the dielectric properties of glucose samples. The frequency-dependent permittivity values of various glucose samples are theoretically estimated using the double-Debye model for sensitivity and uncertainty investigations. The glucose sample concentration is used in the range 70-145mg/dl to mimic healthy human bodies' blood glucose levels, ranging from 70 to 140 mg/dl. The S parameters obtained through SU are converted to the time domain to obtain real-valued impulse responses, which are normalized in PU, making the input data suitable for analysis using Levenberg-Marquardt (LM) algorithm-based Back Propagation Neural Network. The proposed SU. provides a sensitivity of 2 dB for 15 mg/dl change in glucose concentration, and PU exhibits an accuracy of +/- 5%, which falls within the clinical range specified for non-invasive based monitoring systems for diabetes. Overall, SU provides high sensitivity towards blood glucose measurement, and PU enhances the measurement system's readability by forming a non-linear relationship between S parameters and glucose concentration values

Automated summary

This paper presents a non-invasive sub-Terahertz glucose concentration measurement system consisting of Sensor Unit (SU) and Processing Unit (PU). The sensor unit uses waveguide probe sensors to obtain S parameters of glucose samples, while the processing unit analyzes the data using a Levenberg-Marquardt algorithm-based neural network. The system shows high sensitivity and accuracy within the clinical range specified for diabetes monitoring.