Non-invasive blood glucose monitoring using near infrared spectroscopy

Gayathri, B.; Sruthi, K; Menon, Krishnakumar N.

doi:10.1109/iccsp.2017.8286555

Cited by 27 publications

(9 citation statements)

References 6 publications

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“…In recent years, there has been increasing interest in using NIR photoplethysmography (PPG), the oscillating optical signal due to changes in blood volume [67]. The reflectance PPG peak in human subjects can produce an R 2 value of 0.8 for postprandial glucose levels [68] with glucose concentration most strongly correlated to peak PPG wave height compared to other PPG variables, demonstrating an R 2 value of 93% with an SEP of 0.23 mmol/L in one study [69]. In the 530-950 nm region, the PPG wave peak has been used to measure blood glucose concentration and applying Partial Least Squares (PLS) analysis obtained an R 2 value of 0.86 and SEPs of 0.3 mmol/L or less [70].…”

Section: Near Infrared Spectroscopymentioning

confidence: 99%

Recent Progress and Perspectives on Non-Invasive Glucose Sensors

et al. 2022

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Self-monitoring of blood glucose forms an important part of the management of diabetes and the prevention of hyperglycaemia and hypoglycaemia. Current glucose monitoring methods either use needle-prick enzymatic glucose-meters or subcutaneous continuous glucose sensors (CGM) and thus, non-invasive glucose measurements could greatly improve the self-management of diabetes. A wide range of non-invasive sensing techniques have been reported, though achieving a level of precision comparable to invasive meters remains a challenge. Optical sensors, which utilise the interactions between glucose and light, offer the potential for non-invasive continuous sensing, allowing real-time monitoring of glucose levels, and a range of different optical sensing technologies have been proposed. These are primarily based upon optical absorption and scattering effects and include infrared spectroscopy, Raman spectroscopy and optical coherence tomography (OCT), with other optical techniques such as photoacoustic spectroscopy (PAS) and polarimetry also reported. This review aims to discuss the current progress behind the most reported optical glucose sensing methods, theory and current limitations of optical sensing methods and the future technology development required to achieve an accurate optical-based glucose monitoring device.

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Section: Near Infrared Spectroscopymentioning

confidence: 99%

Recent Progress and Perspectives on Non-Invasive Glucose Sensors

et al. 2022

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“…The development of a method of NIR spectroscopy for the continuous monitoring of glucose concentration was proposed in [20]. An improved algorithm was consequently presented as a superior and more accurate means of estimating blood-glucose concentration.…”

Section: -4-near-infrared (Nir) Spectroscopymentioning

confidence: 99%

Acoustic Photometry of Biomedical Parameters for Association with Diabetes and Covid-19

et al. 2022

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Because of their mortality rate, diabetes and COVID-19 are serious diseases. Moreover, people with diabetes are at a higher risk of developing COVID-19 complications. This article therefore proposes a single, non-invasive system that can help people with diabetes and COVID-19 to monitor their health parameters by measuring oxygen saturation (SPO2), heart rate, and body temperature. This is in contrast to other pulse oximeters and previous work reported in the literature. A Max30102 sensor, consisting of two light-emitting diodes (LEDs), can serve as a transmission spectrum to enable three synchronous parameter measurements. Hence, the Max30102 sensor facilitates identification of the relationship between COVID-19 and diabetes in a cost-effective manner. Fifty subjects (20 healthy, 20 diabetic, and 10 with COVID-19), aged 18-61 years, were recruited to provide data on heart rate, body temperature, and oxygen saturation, measured in a variety of activities and scenarios. The results showed accuracy of ±97% for heart rate, ±98% for body temperature, and ±99% for oxygen saturation with an enhanced time efficiency of 5-7 seconds in contrast to a commercialized pulse oximeter, which took 10-12 seconds. The results were then compared with those of commercially available pulse oximetry (Oxitech Pulse Oximeter) and a thermometer (Medisana Infrared Thermometer). These results revealed that uncontrolled diabetes can be as dangerous as COVID-19 in terms of high resting heart rate and low oxygen saturation. Doi: 10.28991/esj-2022-SPER-04 Full Text: PDF

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“…After smoothing the PPG signal using the Butterworth filter [22,23], the calibration model between the PPG data and reference values of BGC was built as shown in Figure 9. The mean value of voltage is calculated from the peaks of PPG data obtained, as there exists a functional relationship between the PPG signal and blood glucose level [24], the voltage intensity of PPG signal changes with variation in glucose concentration. Ten means voltage is calculated from ten PPG readings for the same subject and put into a vector and put the ten reals GCB readings for the same subject into a vector, then used the two vectors as input data for constructing the regression model.…”

Section: Data Treatmentmentioning

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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Non-invasive blood glucose monitoring using near infrared spectroscopy

Cited by 27 publications

References 6 publications

Recent Progress and Perspectives on Non-Invasive Glucose Sensors

Recent Progress and Perspectives on Non-Invasive Glucose Sensors

Acoustic Photometry of Biomedical Parameters for Association with Diabetes and Covid-19

Noninvasive blood glucose monitoring system based on near-infrared method

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