Unsupervised calibration for noninvasive glucose-monitoring devices using mid-infrared spectroscopy

Kasahara, Ryosuke; Kino, Saiko; Soyama, Shunsuke; Matsuura, Yuji

doi:10.1142/s1793545818500384

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…We are currently working on experiments with more subjects and further improvements to the shape of the prism. In addition, we are also working on optimizing the selected wavelength by using regression analysis or a neural network to improve accuracy [29] [30].…”

Section: Discussionmentioning

confidence: 99%

Accuracy Improvement of Blood Glucose Measurement System Using Quantum Cascade Lasers

Kõyama¹,

Kino²,

Matsuura³

2019

OPJ

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For non-invasive measurement of blood glucose levels, a measurement system based on mid-infrared, attenuated-total-reflection spectroscopy equipped with hollow optical fibers, a trapezoidal multi-reflection prism, and two fixedwavelength quantum cascade lasers emitting different wavelengths is proposed. From the absorption spectra of lip mucosa measured by Fouriertransform infrared spectrometry, two wavelengths, 1152 cm −1 for absorption by glucose and 1186 cm −1 for the background, were chosen. To reduce measurement errors, the power distribution on the prism surface was investigated, and it was found that some high-intensity spots appear on the prism surface due to the coherency of the laser beam. This inhomogeneous power distribution causes measurement errors for slight movements of the lip mucosa. To homogenize the intensity distribution on the prism, a lens to excite higher modes in the fiber was introduced, and the incident angle was changed to suppress interference due to back-reflected light. These improvements increased the measurement stability, and in-vivo experiments demonstrated that the measured optical absorption correlates well with blood glucose levels.

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Section: Discussionmentioning

confidence: 99%

Accuracy Improvement of Blood Glucose Measurement System Using Quantum Cascade Lasers

Kõyama¹,

Kino²,

Matsuura³

2019

OPJ

Self Cite

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“…In addition, glucose makes up only 0.07-0.1% of plasma, and other components of tissues or blood (including water, pigments, proteins, etc.) can affect the amount of light absorbed and interfere with the determination of blood glucose [51][52][53][54]. Therefore, the wavelength of the light source needs to be chosen in a range that is as highly specifically absorbed by glucose as possible.…”

Section: Near-infrared (Nir) and Mid-infrared (Mir) Spectroscopymentioning

confidence: 99%

Non-Invasive Blood Glucose Monitoring Technology: A Review

Liu

Chang

Chen

et al. 2020

Sensors

259

157

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In recent years, with the rise of global diabetes, a growing number of subjects are suffering from pain and infections caused by the invasive nature of mainstream commercial glucose meters. Non-invasive blood glucose monitoring technology has become an international research topic and a new method which could bring relief to a vast number of patients. This paper reviews the research progress and major challenges of non-invasive blood glucose detection technology in recent years, and divides it into three categories: optics, microwave and electrochemistry, based on the detection principle. The technology covers medical, materials, optics, electromagnetic wave, chemistry, biology, computational science and other related fields. The advantages and limitations of non-invasive and invasive technologies as well as electrochemistry and optics in non-invasives are compared horizontally in this paper. In addition, the current research achievements and limitations of non-invasive electrochemical glucose sensing systems in continuous monitoring, point-of-care and clinical settings are highlighted, so as to discuss the development tendency in future research. With the rapid development of wearable technology and transdermal biosensors, non-invasive blood glucose monitoring will become more efficient, affordable, robust, and more competitive on the market.

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“…Han et al [26] proposed a hybrid model to improve the prediction accuracy of BGC by using the integrated linear partial least square regression (PLSR) with the nonlinear stacked auto‐encoder deep neural network. Kasahara et al [27] utilized MIR spectroscopy to obtain the spectra of blood glucose, and used deep neural networks to train a regression model of BGC to improve the correlation coefficient between the true BGC and predicted ones. Kim et al [28] used MIR spectroscopy to determine the glucose concentration of whole blood, and used the partial least square regression (PLSR) to achieve the glucose prediction.…”

Section: Introductionmentioning

confidence: 99%

Quantitative measurement of blood glucose influenced by multiple factors via photoacoustic technique combined with optimized wavelet neural networks

Ren

Liu

Xiong

et al. 2022

Journal of Biophotonics

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In this work, the photoacoustic (PA) quantitative measurement of blood glucose concentration (BGC) influenced by multiple factors was firstly investigated. A set of PA detection system of blood glucose considering the comprehensive influence of five factors was established. The PA signals and peak‐to‐peak values (PPVs) of 625 rabbit whole blood were obtained under 625 influence combinations. Due to the accurate measurement of BGC limited by the overlap PA signals, wavelet neural network (WNN) was utilized to train the PPVs of blood glucose for 500 rabbit blood. The mean square error (MSE) of BGC for 125 testing blood was approximately 6.5782 mmol/L. To decrease the MSE, the parameters of WNN were optimized by particle swarm optimization (PSO), that is, PSO‐WNN algorithm was employed. Under the optimal parameters, MSE of BGC was decreased to approximately 0.48005 mmol/L. To further improve the prediction accuracy of BGC, an improved nonlinear dynamic inertia weight (NDIW) strategy of PSO was proposed, and compared with other two kinds of dynamic inertia weight strategies. Under the optimal parameters, the MSE of BGC was decreased to approximately 0.2635 mmol/L. The comparison of nine algorithms demonstrate that the PA technique combined with PSO‐WNN and the improved NDIW strategy is significant in the quantitative measurement of blood glucose influenced by multiple factors.

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Unsupervised calibration for noninvasive glucose-monitoring devices using mid-infrared spectroscopy

Cited by 9 publications

References 29 publications

Accuracy Improvement of Blood Glucose Measurement System Using Quantum Cascade Lasers

Accuracy Improvement of Blood Glucose Measurement System Using Quantum Cascade Lasers

Non-Invasive Blood Glucose Monitoring Technology: A Review

Quantitative measurement of blood glucose influenced by multiple factors via photoacoustic technique combined with optimized wavelet neural networks

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