Glucose detection using an electro-optical fluidic device based on pulse width modulation

Tang, Jing-Yau; Wang, Min-Haw; Chen, Ming-Kun; Jang, Ling-Sheng

doi:10.1109/icsenst.2013.6727668

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…IMF 1 = x − r 1 (4) where x is the original signal, β 0 is the standard deviation of the added white Gaussian noise, and E k (·) is the operator that produces the k-th mode obtained by EMD algorithm (k = 1,2, . .…”

Section: Improved Ceemdan Methodsmentioning

confidence: 99%

“…Given the development of computer technology and chemometrics in recent years, high efficiency and low-cost near-infrared spectra technologies that can perform fast analysis are widely used in non-invasive blood glucose detection [3]. The electromagnetic wavelength near-infrared light between visible light and medium infrared light ranges from 700 to 2500 nm [4]. For example, glucose molecules contain C-H, N-H, and O-H groups, and the stretching (2) Obtain the upper envelope composed of all the maxima and the lower envelope composed of all the minima using the cubic spline interpolation, and define them as u(t) and v(t) respectively.…”

Section: Introductionmentioning

confidence: 99%

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Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

2017

Entropy

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Abstract:The noise of near-infrared spectra and spectral information redundancy can affect the accuracy of calibration and prediction models in near-infrared analytical technology. To address this problem, the improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and permutation entropy (PE) were used to propose a new method for pretreatment and wavelength selection of near-infrared spectra signal. The near-infrared spectra of glucose solution was used as the research object, the improved CEEMDAN energy entropy was then used to reconstruct spectral data for removing noise, and the useful wavelengths are selected based on PE after spectra segmentation. Firstly, the intrinsic mode functions of original spectra are obtained by improved CEEMDAN algorithm. The useful signal modes and noisy signal modes were then identified by the energy entropy, and the reconstructed spectral signal is the sum of useful signal modes. Finally, the reconstructed spectra were segmented and the wavelengths with abundant glucose information were selected based on PE. To evaluate the performance of the proposed method, support vector regression and partial least square regression were used to build the calibration model using the wavelengths selected by the new method, mutual information, successive projection algorithm, principal component analysis, and full spectra data. The results of the model were evaluated by the correlation coefficient and root mean square error of prediction. The experimental results showed that the improved CEEMDAN energy entropy can effectively reconstruct near-infrared spectra signal and that the PE can effectively solve the wavelength selection. Therefore, the proposed method can improve the precision of spectral analysis and the stability of the model for near-infrared spectra analysis.

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Section: Improved Ceemdan Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

2017

Entropy

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show abstract

“…Near-infrared spectroscopy (NIR), which is generated from molecular vibrations and reflects the chemical bond information, such as C-H, O-H, N-H, and S-H, can measure most kinds of compounds and their mixtures. Compared with the traditional analytical techniques, NIR has been widely applied because it is highly efficient and causes no damage and pollution [6][7][8]. The main structure and composition of glucose information is contained in the near-infrared spectra.…”

Section: Introductionmentioning

confidence: 99%

Application of Permutation Entropy in Feature Extraction for Near-Infrared Spectroscopy Noninvasive Blood Glucose Detection

2017

Journal of Spectroscopy

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Diabetes has been one of the four major diseases threatening human life. Accurate blood glucose detection became an important part in controlling the state of diabetes patients. Excellent linear correlation existed between blood glucose concentration and near-infrared spectral absorption. A new feature extraction method based on permutation entropy is proposed to solve the noise and information redundancy in near-infrared spectral noninvasive blood glucose measurement, which affects the accuracy of the calibration model. With the near-infrared spectral data of glucose solution as the research object, the concepts of approximate entropy, sample entropy, fuzzy entropy, and permutation entropy are introduced. The spectra are then segmented, and the characteristic wave bands with abundant glucose information are selected in terms of permutation entropy, fractal dimension, and mutual information. Finally, the support vector regression and partial least square regression are used to establish the mathematical model between the characteristic spectral data and glucose concentration, and the results are compared with conventional feature extraction methods. Results show that the proposed new method can extract useful information from near-infrared spectra, effectively solve the problem of characteristic wave band extraction, and improve the analytical accuracy of spectral and model stability.

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Sensing Technologies for Intelligent Environments: A Review

Ghayvat

Mukhopadhyay

Gui

2015

Smart Sensors, Measurement and Instrumentation

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Glucose detection using an electro-optical fluidic device based on pulse width modulation

Cited by 4 publications

References 17 publications

Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

Application of Permutation Entropy in Feature Extraction for Near-Infrared Spectroscopy Noninvasive Blood Glucose Detection

Sensing Technologies for Intelligent Environments: A Review

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