Comparison analysis between PLS and NN in noninvasive blood glucose concentration prediction

Ming, Chuah Zheng; Raveendran, P.

doi:10.1109/techpos.2009.5412048

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Section 1 presents the best results in the literature. Our experimental results prove that our chosen technique as well as the regression model are better than the other methods (Parab et al, 2010;Uwadaira et al, 2015;Mougiakakou et al, 2006;Perez-Gandia et al, 2010;Robertson et al, 2011;Pappada et al, 2011;Zecchin et al, 2012;Ming and Raveendran, 2009;Zhua et al, 2019;Wu et al, 2017).…”

Section: Discussionsupporting

confidence: 53%

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Comparison of Linear and Non-Linear Regression Models for Non-Invasive Blood Glucose Measurement

Dorsaf¹,

Manai²,

Nouri³

2019

Journal of Computer Science

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This paper deals with a new approach of non-invasive glucose monitoring based on near infrared spectroscopy. The proposed approach is coupled with a regression analysis in order to improve the predictive capacity of the designed device. Basic spectral data is a comparison that has been established between linear and non-linear machine learning regression algorithms. The experimental results show that feed forward backpropagation neural network improves more the performance of the designed prototype than partial least square models. The squared correlation coefficient and the Root Mean Square Error (RMSE) of the Artificial Neural Network (ANN) regression model built were 0.9804 and 0.0784 respectively. The ANN regression model was then used in the validation step using 300 human serums with a concentration range of 08-297 mg/dl. Clarke Error Grid Analysis (EGA) showed that 97% of the measured concentrations fall within the clinically acceptable regions. Results showed that the created model can open a new path to a noninvasive glucose monitoring.

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Section: Discussionsupporting

confidence: 53%

“…To choose the best calibration, (Ming and Raveendran, 2009) used two different techniques: PLS and feed forward backpropagation neural network. The RMSE is around 0.5282 mmol/L and 0.2952 mmol/L, respectively.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Linear and Non-Linear Regression Models for Non-Invasive Blood Glucose Measurement

Dorsaf¹,

Manai²,

Nouri³

2019

Journal of Computer Science

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“…As shown in Figure 6, after predicting the blood glucose of Day 10 of all 12 volunteers with PCA and BP-ANN, we plotted the predicted and reference values in the Clarke error grid. The Clarke error grid was established by Clarke and co-workers to evaluate the clinical utility of systems for blood glucose monitoring [52,53,54]. Now it is usually used to assess the accuracy of blood glucose measurements to the standard reference value.…”

Section: Resultsmentioning

confidence: 99%

A Noninvasive Accurate Measurement of Blood Glucose Levels with Raman Spectroscopy of Blood in Microvessels

Zang

Sun

et al. 2019

Molecules

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Raman spectra of human skin obtained by laser excitation have been used to non-invasively detect blood glucose. In previous reports, however, Raman spectra thus obtained were mainly derived from the epidermis and interstitial fluid as a result of the shallow penetration depth of lasers in skin. The physiological process by which glucose in microvessels penetrates into the interstitial fluid introduces a time delay, which inevitably introduces errors in transcutaneous measurements of blood glucose. We focused the laser directly on the microvessels in the superficial layer of the human nailfold, and acquired Raman spectra with multiple characteristic peaks of blood, which indicated that the spectra obtained predominantly originated from blood. Incorporating a multivariate approach combining principal component analysis (PCA) and back propagation artificial neural network (BP-ANN), we performed noninvasive blood glucose measurements on 12 randomly selected volunteers, respectively. The mean prediction performance of the 12 volunteers was obtained as an RMSEP of 0.45 mmol/L and R2 of 0.95. It was no time lag between the predicted blood glucose and the actual blood glucose in the oral glucose tolerance test (OGTT). We also applied the procedure to data from all 12 volunteers regarded as one set, and the total predicted performance was obtained with an RMSEP of 0.27 mmol/L and an R2 of 0.98, which is better than that of the individual model for each volunteer. This suggested that anatomical differences between volunteer fingernails do not reduce the prediction accuracy and 100% of the predicted glucose concentrations fall within Region A and B of the Clarke error grid, allowing acceptable predictions in a clinically relevant range. The Raman spectroscopy detection of blood glucose from microvessels is of great significance of non-invasive blood glucose detection of Raman spectroscopy. This innovative method may also facilitate non-invasive detection of other blood components.

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“…We expect that further absolute MARD reduction can be achieved by combining the techniques presented in this work with more sophisticated NIR hardware that has been presented in the literature [ 40 ]. As an example, NIR systems in the same wavelength range examined here, unassisted by RF sensing, reported accuracy errors on the range of ±15–50 mg/dl [ 41 , 42 , 43 , 44 ]. Adding mm-wave RF sensing to equivalent systems could be enough to bring them within a clinical accuracy range, since this is the first multi-modal system that utilizes transmission of radio waves to enhance sensing accuracy.…”

Section: Discussionmentioning

confidence: 96%

Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission

Cano-Garcia

Kshirsagar

Pricci

et al. 2021

Sensors

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We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

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Comparison analysis between PLS and NN in noninvasive blood glucose concentration prediction

Cited by 6 publications

References 17 publications

Comparison of Linear and Non-Linear Regression Models for Non-Invasive Blood Glucose Measurement

Comparison of Linear and Non-Linear Regression Models for Non-Invasive Blood Glucose Measurement

A Noninvasive Accurate Measurement of Blood Glucose Levels with Raman Spectroscopy of Blood in Microvessels

Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission

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