Non-invasive Estimation of Blood Glucose Level in Visible-NIR Spectrum: System and Software Design

Sen, Prasanta Kr; Laskar, Mostafizur Rahaman; Mandal, Shyamal Kumar Das

doi:10.1109/wispnet45539.2019.9032840

Cited by 1 publication

(1 citation statement)

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“…Because blood glucose has different absorption rates for light sources of different wavelengths, Sen et al proposed a novel design to detect blood glucose through three sensors using light sources with wavelengths of 940 nm, 660 nm, and 660 nm. They used two sensors (940 nm and 660 nm) to obtain the refraction signal and a third sensor (660 nm) to acquire the reflection signal, after which they conducted data analyses using analyses of variance [7]. Deepthi et al used a 940 nm infrared light and proposed a blood glucose monitoring method that outperformed the invasive measurement approaches used by hospitals with a percentage error of ±2.5%, indicating that the model can favorably predict blood glucose concentrations [8].…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

Yen

Chen

Wang

et al. 2022

Sensors

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This study proposed a noninvasive blood glucose estimation system based on dual-wavelength photoplethysmography (PPG) and bioelectrical impedance measuring technology that can avoid the discomfort created by conventional invasive blood glucose measurement methods while accurately estimating blood glucose. The measured PPG signals are converted into mean, variance, skewness, kurtosis, standard deviation, and information entropy. The data obtained by bioelectrical impedance measuring consist of the real part, imaginary part, phase, and amplitude size of 11 types of frequencies, which are converted into features through principal component analyses. After combining the input of seven physiological features, the blood glucose value is finally obtained as the input of the back-propagation neural network (BPNN). To confirm the robustness of the system operation, this study collected data from 40 volunteers and established a database. From the experimental results, the system has a mean squared error of 40.736, a root mean squared error of 6.3824, a mean absolute error of 5.0896, a mean absolute relative difference of 4.4321%, and a coefficient of determination (R Squared, R2) of 0.997, all of which fall within the clinically accurate region A in the Clarke error grid analyses.

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Section: Introductionmentioning

confidence: 99%