Noninvasive Blood Glucose Sensing Using Near Infra-Red Spectroscopy and Artificial Neural Networks Based on Inverse Delayed Function Model of Neuron

Ramasahayam, Swathi; Koppuravuri, Sri Haindavi; Arora, Lavanya; Chowdhury, Shubhajit Roy

doi:10.1007/s10916-014-0166-2

Cited by 41 publications

(19 citation statements)

References 29 publications

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“…However, due to the low absorption values of the overtones, the double peaks cannot be observed. The low absorption values of the overtones are due to the lower probability of the absorption of the photons by the molecules at a higher vibrational energy level [24]. Hence, the magnitude of the absorption decreases with increasing overtones.…”

Section: Assignment Of Functional Groupsmentioning

confidence: 98%

Detection of 2FAL Furanic Compound in Transformer Oil Using Optical Spectroscopy Method and Verification Using Morse Oscillation Theory

et al. 2020

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The condition of a power transformer can be predicted based on the contents of the transformer oil. One of the most common parameters that are observed in the transformer oil is 2-Furaldehyde, a compound that is produced due to the aging of Kraft paper. Although the optical characterization of 2-Furaldehyde has been widely reported, the fundamental theories of the observation of the optical absorbance peaks have not been elaborated. This study investigates the optical characteristics of 2-Furaldehyde in the near infrared region. Ten samples with different concentrations of 2-Furaldehyde were prepared and verified using the conventional method. The samples were then characterized using optical spectroscopy method with 50 mm path length cuvettes. Three peaks were observed at 1610 nm-1640 nm, 1100 nm-1115 nm and 860 nm-890 nm wavebands and were correlated to the overtones of the aromatic C-H stretch in 2-Furaldehyde. The appearance of these overtones corresponding to the Morse Oscillation Theory was discussed. This fundamental knowledge is significant in developing a portable optical device that enables the detection of 2-Furaldehyde on site.

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Section: Assignment Of Functional Groupsmentioning

confidence: 98%

Detection of 2FAL Furanic Compound in Transformer Oil Using Optical Spectroscopy Method and Verification Using Morse Oscillation Theory

et al. 2020

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“…To compensate for this nonlinearity, the ANN is ideal model for approximating such functions [4,10]. Initially, the back propagation artificial neural network comprising of two layers is chosen.…”

Section: Data Processingmentioning

confidence: 99%

“…ANNs are used to solve variety of problems like pattern recognition, prediction, and optimization [4][5][6][7]. Most of the existing models of neural networks interpret biological behaviour in terms of time averaging techniques rather than directly emulating them.…”

Section: Introductionmentioning

confidence: 99%

Detection of Whole Blood Glucose Concentration by Fourier-Transform Raman Spectroscopy and Artificial Neural Networks

Wang¹,

Wu²,

Shan³

et al. 2017

Proceedings of the 2017 7th International Conference on Applied Science, Engineering and Technology (ICASET 2017)

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Abstract. Objectives: Diabetes, which is caused by the disorder of blood glucose, has been one of the most important metabolic diseases worldwide. In order to avoid the diabetes-related complications, such as blindness and loss of limbs, the diet adjustment and insulin therapy are need to check glucose level at least four to five times a day. The aim of this paper was to investigate the potential of Fourier-Transform Raman spectroscopy as a diagnostic tool to monitor the blood glucose, triglycerides and total cholesterol levels. Methods: Raman spectroscopy were acquired from 116 individuals with diabetes patients, patients and healthy patients in order to gain an insight into the determination of biochemical changes for the diabetes diagnose. The human whole blood was examined at 1064nm excitation laser source. Results: A new method with modified sample selection algorithm named (K-Means) KM is developed and applied to the Raman spectroscopy of whole blood. The algorithms have been successfully applied to improve the prediction accuracy. This method can detect the glucose concentration in the human whole blood without complicated sample preparation procedures. Conclusions: The experimental results show that the Raman spectroscopy technology has enormous clinical potential as a rapid diagnostic tool for diabetes diseases.

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“…Firstly, the drastic increase in the cost of components and secondly the strong absorption due to other components like water and lastly the scattering in fatty tissue. in the second overtone region which is used in the present work, the components are less affordable, tissue absorption is lower and light penetration is higher, but glucose has weaker absorption and it needs the electronic noise in the analog sensing circuit to be suppressed much below the signal from the sensor output [11,12].…”

Section: Introductionmentioning

confidence: 99%

Noninvasive blood glucose monitoring system based on near-infrared method

Al-dhaheri¹,

Mekkakia-Maaza²,

Mouhadjer³

et al. 2020

IJECE

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Diabetes is considered one of the life-threatening diseases in the world which need continuous monitoring to avoid the complication of diabetes. There is a need to develop a non-invasive monitoring system that avoids the risk of infection problems and pain caused by invasive monitoring techniques. This paper presents a method for developing a noninvasive technique to predict the blood glucose concentration (BCG) based on the Near-infrared (NIR) light sensor. A prototype is developed using a finger sensor based on LED of 940 nm wavelength to collect photoplethysmography (PPG) signal which is variable depending on the glucose concentration variance, a module circuit to preprocess PPG signals is realized, which includes an amplifier and analog filter circuits, an Arduino UNO is used to analog-to-digital conversion. A digital Butterworth filterer is used to remove PPG signal trends, then detect the PPG data peaks to determine the relationship between the PPG signal and (BCG) and use it as input parameters to build the calibration model based on linear regression. Experiments show that the Root Mean Squares Error (RMSE) of the prediction is between 8.264mg/dL and 13.166 mg/dL, the average of RMSE is about 10.44mg/dL with a correlation coefficient (R^2) of 0.839, it is observed that the prediction of glucose concentration is in the clinically acceptable region of the standard Clark Error Grid (CEG).

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Noninvasive Blood Glucose Sensing Using Near Infra-Red Spectroscopy and Artificial Neural Networks Based on Inverse Delayed Function Model of Neuron

Cited by 41 publications

References 29 publications

Detection of 2FAL Furanic Compound in Transformer Oil Using Optical Spectroscopy Method and Verification Using Morse Oscillation Theory

Detection of 2FAL Furanic Compound in Transformer Oil Using Optical Spectroscopy Method and Verification Using Morse Oscillation Theory

Detection of Whole Blood Glucose Concentration by Fourier-Transform Raman Spectroscopy and Artificial Neural Networks

Noninvasive blood glucose monitoring system based on near-infrared method

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