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

Cano-Garcia, Helena; Kshirsagar, Rohit; Pricci, Roberto L.; Teyeb, Ahmed; O'Brien, Fergus; Saha, Subhajit; Kosmas, Panagiotis; Kallos, Efthymios

doi:10.3390/s21093275

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…Multiwavelength sensing using two modalities such as RF mmW with NIR transmission has also been found to significantly increase accuracy and sensitivity, while also supporting the capacity to be a wearable continuous monitoring device [68,117,118]. However, when combining sensors, this can increase the circuit space and comes with a risk of either error or interference.…”

Section: Radio Frequency (Rf) Spectroscopymentioning

confidence: 96%

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians

Di Filippo,

Sunstrum,

Khan

et al. 2023

Sensors

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Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques’ research and production to aid the progress in this promising field.

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Section: Radio Frequency (Rf) Spectroscopymentioning

confidence: 96%

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians

Di Filippo,

Sunstrum,

Khan

et al. 2023

Sensors

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“…The absorption of variable AC light is mainly due to the pulsating volume of arterial blood. Helena Cano-Garcia et al designed a novel multiwavelength sensor by combining RF signals and Near-infrared spectroscopy [60] (Figure2.d). The sensor consists of two patch antennas and two optical fibers, which can simultaneously collect data about glucose levels in the electromagnetic wave segment of 37-39 GHz and 900-1800 nm.…”

Section: Near-infrared Spectroscopy-based Sensormentioning

confidence: 99%

Non-invasive blood glucose sensors based on electromagnetic wave

2023

acss

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Low-cost, reusable, highly compliant and non-invasive blood glucose sensors (NIBGS) are necessary for current and future medical systems. This review aims to introduce various NIBGS with wide application prospect and focus on electromagnetic wave-based NIBGS. The main introduction is based on Near-infrared spectroscopy-based, Raman spectroscopy-based and microwave-based sensors. The basic working principle of each type of sensor is explained, and their structure, detection accuracy, detection range and detection site are also summarized. The improvements and impacts made by various types of non-invasive testing applications are also analyzed. Several commonly used performance evaluation methods of non-invasive blood glucose monitoring (NIBGM) are introduced. With the development of electronic device and intelligent algorithms, NIBGM will continue to expand its advantages, and the creation of new practical applications will greatly improve the healthcare environment and bring great convenience to the lifestyle of patients with diabetes.

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“…Published literatures suggest that there has been some attempt to construct glucose sensor by introducing near-infrared (NIR) glucose sensing but these used only single or multiple-band NIR (mbNIR) to train Neural Networks (NN), and thereby generated insufficient accuracy 5 . Carno-Garcia et al (2021) reported using combination of NIR and 37–39 GHz frequency to excite and measure response for glucose determination together with ANN; however, the experiment was done in aquas solution, in vitro 6 .…”

Section: Introductionmentioning

confidence: 99%

Non-invasively accuracy enhanced blood glucose sensor using shallow dense neural networks with NIR monitoring and medical features

Srichan

Danvirutai³

et al. 2022

Sci Rep

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Non-invasive and accurate method for continuous blood glucose monitoring, the self-testing of blood glucose is in quest for better diagnosis, control and the management of diabetes mellitus (DM). Therefore, this study reports a multiple photonic band near-infrared (mbNIR) sensor augmented with personalized medical features (PMF) in Shallow Dense Neural Networks (SDNN) for the precise, inexpensive and pain free blood glucose determination. Datasets collected from 401 blood samples were randomized and trained with ten-fold validation. Additionally, a cohort of 234 individuals not included in the model training set were investigated to evaluate the performance of the model. The model achieved the accuracy of 97.8% along with 96.0% precision, 94.8% sensitivity and 98.7% specificity for DM classification based on a diagnosis threshold of 126 mg/dL for diabetes in fasting blood glucose. For non-invasive real-time blood glucose monitoring, the model exhibited ± 15% error with 95% confidence interval and the detection limit of 60–400 mg/dL, as validated with the standard hexokinase enzymatic method for glucose estimation. In conclusion, this proposed mbNIR based SDNN model with PMF is highly accurate and computationally cheaper compared to similar previous works using complex neural network. Some groups proposed using complicated mixed types of sensors to improve noninvasive glucose prediction accuracy; however, the accuracy gain over the complexity and costs of the systems harvested is still in questioned (Geng et al. in Sci Rep 7:12650, 2017). None of previous works report on accuracy enhancement of NIR/NN using PMF. Therefore, the proposed SDNN over PMF/mbNIR is an extremely promising candidate for the non-invasive real-time blood glucose monitoring with less complexity and pain-free.

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

Cited by 9 publications

References 38 publications

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians

Non-invasive blood glucose sensors based on electromagnetic wave

Non-invasively accuracy enhanced blood glucose sensor using shallow dense neural networks with NIR monitoring and medical features

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