Non-invasive Blood Glucose Monitoring and Data Analytics

Eadie, Melissa; Steele, Robert

doi:10.1145/3093241.3093283

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…Data analytics in processing various glucose properties for noninvasive and minimally invasive techniques is an emerging technology [120] contributing to the field of diabetes informatics and providing a more data-rich approach to understanding and managing diabetes.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

et al. 2020

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Diabetes is one of today’s greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend.

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Section: Discussionmentioning

confidence: 99%

Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

et al. 2020

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“…For example, if the data set is linear, algorithms such as principal component analysis (PCA), partial least squares (PLS) and least absolute shrinkage (LASSO) are efficient [ 184 ]. However, if the data has a non-linear nature, such as Raman spectroscopy, non-linear algorithms, such as logistic regression (LR) and support vector machines (SVM), are more convenient [ 185 ]. Besides, machine learning and artificial neural networks have been receiving much attention since they can describe problems that cannot be handled by highly non-linear algorithms or in situations in which variables evolve or mutate from the initial conditions [ 183 ].…”

Section: Glucose Monitoring Informatics (Gmi)mentioning

confidence: 99%

“…As explained in the previous section, using prediction algorithms makes it possible to forecast future levels of glucose in short and long terms (from less than a couple of minutes to several minutes), meaning that states such as hypoglycaemia and hyperglycaemia can be predicted and safely managed. This approach opens the door to the so-called ‘artificial pancreas’ or ‘closed-loop’ systems, which pump a certain amount of insulin automatically into the patient according to the forecasted value of glucose level in order to avoid critical glycaemic states [ 185 ]. Those systems are highly useful for type-1 patients [ 178 ].…”

Section: Glucose Monitoring Informatics (Gmi)mentioning

confidence: 99%

The Progress of Glucose Monitoring—A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors

Gonzales

Mobashsher

Abbosh

2019

Sensors

505

310

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Current glucose monitoring methods for the ever-increasing number of diabetic people around the world are invasive, painful, time-consuming, and a constant burden for the household budget. The non-invasive glucose monitoring technology overcomes these limitations, for which this topic is significantly being researched and represents an exciting and highly sought after market for many companies. This review aims to offer an up-to-date report on the leading technologies for non-invasive (NI) and minimally-invasive (MI) glucose monitoring sensors, devices currently available in the market, regulatory framework for accuracy assessment, new approaches currently under study by representative groups and developers, and algorithm types for signal enhancement and value prediction. The review also discusses the future trend of glucose detection by analyzing the usage of the different bands in the electromagnetic spectrum. The review concludes that the adoption and use of new technologies for glucose detection is unavoidable and closer to become a reality.

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“…The closely fitted wearable devices on skin can collect body fluids and obtain tissue characteristics, which is significant for the detection of human physiological indicators. For example, the combination of wearable devices and reverse iontophoresis or electrochemical methods enables the measurement of glucose levels in relevant human body fluids, thus potentially achieving noninvasive CGM. , …”

Section: Noninvasive Cgm Technology Based On Wearable Devicesmentioning

confidence: 99%

Technological Advances of Wearable Device for Continuous Monitoring of In Vivo Glucose

Huang,

Yao,

Huang

et al. 2024

ACS Sens.

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Managing diabetes is a chronic challenge today, requiring monitoring and timely insulin injections to maintain stable blood glucose levels. Traditional clinical testing relies on fingertip or venous blood collection, which has facilitated the emergence of continuous glucose monitoring (CGM) technology to address data limitations. Continuous glucose monitoring technology is recognized for tracking long-term blood glucose fluctuations, and its development, particularly in wearable devices, has given rise to compact and portable continuous glucose monitoring devices, which facilitates the measurement of blood glucose and adjustment of medication. This review introduces the development of wearable CGM-based technologies, including noninvasive methods using body fluids and invasive methods using implantable electrodes. The advantages and disadvantages of these approaches are discussed as well as the use of microneedle arrays in minimally invasive CGM. Microneedle arrays allow for painless transdermal puncture and are expected to facilitate the development of wearable CGM devices. Finally, we discuss the challenges and opportunities and look forward to the biomedical applications and future directions of wearable CGM-based technologies in biological research.

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Non-invasive Blood Glucose Monitoring and Data Analytics

Cited by 16 publications

References 20 publications

Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

The Progress of Glucose Monitoring—A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors

Technological Advances of Wearable Device for Continuous Monitoring of In Vivo Glucose

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