Non-Invasive Blood Glucose Monitoring Technology: A Review

Liu, Tang; Chang, Shwu Jen; Chen, Ching-Jung; Liu, Jen‐Tsai

doi:10.3390/s20236925

Cited by 226 publications

(126 citation statements)

References 146 publications

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“…All commercial CGM devices are labeled as minimally invasive since they require either a microneedle or a small capsule to be inserted in the subcutis, and they represent an important innovation because they allow reducing the burden of performing multiple daily invasive self-monitoring tests of BG concentrations. Of note, in recent years, there has been a great effort in investigating noninvasive glucose monitoring technologies (see [ 3 , 4 , 5 , 6 ] for reviews on the topic). Noninvasive CGM devices represent a further step in reducing the burden related to the daily management of T1D, but unfortunately, they are all still prototypes.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only

Prendin

Favero

Vettoretti

et al. 2021

Sensors

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In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input of this kind of algorithm is often continuous glucose monitoring (CGM) sensor data, because other signals (such as injected insulin, ingested carbs, and physical activity) are frequently unavailable. Several predictive algorithms fed by CGM data only have been proposed in the literature, but they were assessed using datasets originated by different experimental protocols, making a comparison of their relative merits difficult. The aim of the present work was to perform a head-to-head comparison of thirty different linear and nonlinear predictive algorithms using the same dataset, given by 124 CGM traces collected over 10 days with the newest Dexcom G6 sensor available on the market and considering a 30-min prediction horizon. We considered the state-of-the art methods, investigating, in particular, linear black-box methods (autoregressive; autoregressive moving-average; and autoregressive integrated moving-average, ARIMA) and nonlinear machine-learning methods (support vector regression, SVR; regression random forest; feed-forward neural network, fNN; and long short-term memory neural network). For each method, the prediction accuracy and hypoglycemia detection capabilities were assessed using either population or individualized model parameters. As far as prediction accuracy is concerned, the results show that the best linear algorithm (individualized ARIMA) provides accuracy comparable to that of the best nonlinear algorithm (individualized fNN), with root mean square errors of 22.15 and 21.52 mg/dL, respectively. As far as hypoglycemia detection is concerned, the best linear algorithm (individualized ARIMA) provided precision = 64%, recall = 82%, and one false alarm/day, comparable to the best nonlinear technique (population SVR): precision = 63%, recall = 69%, and 0.5 false alarms/day. In general, the head-to-head comparison of the thirty algorithms fed by CGM data only made using a wide dataset shows that individualized linear models are more effective than population ones, while no significant advantages seem to emerge when employing nonlinear methodologies.

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Section: Introductionmentioning

confidence: 99%

Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only

Prendin

Favero

Vettoretti

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Just as in the past, many did not expect that a subcutaneous glucose sensor would ever provide accurate glucose readings comparable to blood glucose testing, many now believe that a wearable optical noninvasive glucose monitor [ 97 ] or a monitor that is based on noninvasive collection and measurement of a body fluid (such as sweat, saliva, or tears) [ 98 ] cannot be developed to measure glucose almost as accurately as an invasive blood test. These doubters of optical noninvasive technology might prove to be just as wrong as the doubters of subcutaneous minimally invasive technology were 20 years ago.…”

Section: Future Technologymentioning

confidence: 99%

Continuous Glucose Monitoring in the Hospital

et al. 2021

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Continuous glucose monitors (CGMs) have suddenly become part of routine care in many hospitals. The coronavirus disease 2019 (COVID-19) pandemic has necessitated the use of new technologies and new processes to care for hospitalized patients, including diabetes patients. The use of CGMs to automatically and remotely supplement or replace assisted monitoring of blood glucose by bedside nurses can decrease: the amount of necessary nursing exposure to COVID-19 patients with diabetes; the amount of time required for obtaining blood glucose measurements, and the amount of personal protective equipment necessary for interacting with patients during the blood glucose testing. The United States Food and Drug Administration (FDA) is now exercising enforcement discretion and not objecting to certain factory-calibrated CGMs being used in a hospital setting, both to facilitate patient care and to obtain performance data that can be used for future regulatory submissions. CGMs can be used in the hospital to decrease the frequency of fingerstick point of care capillary blood glucose testing, decrease hyperglycemic episodes, and decrease hypoglycemic episodes. Most of the research on CGMs in the hospital has focused on their accuracy and only recently outcomes data has been reported. A hospital CGM program requires cooperation of physicians, bedside nurses, diabetes educators, and hospital administrators to appropriately select and manage patients. Processes for collecting, reviewing, storing, and responding to CGM data must be established for such a program to be successful. CGM technology is advancing and we expect that CGMs will be increasingly used in the hospital for patients with diabetes.

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“…It is expected that biosensors and hand-held bioassays will be cheaper that the standard instrumental methods, will be applicable without expensive measuring or sample-processing devices and will require neither elaborative sample or reagents processing nor demands on staff training or education. Currently, there are methods and biosensors available for the rapid detection of glucose and glycemia level determination, and these devices exert good analytical parameters, simplicity and low costs, and noninvasive methods for measuring glucose have even been developed [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ]. Though the methods for measuring glucose are promising and many of them are currently available in the market, they have limitations in the interpretation of glucose level, as discussed in the previous chapter.…”

Section: Biosensors and Bioassays Measuring Hba 1cmentioning

confidence: 99%

Glycated Hemoglobin and Methods for Its Point of Care Testing

Pohanka

2021

Biosensors

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Glycated hemoglobin (HbA1c) is a product of the spontaneous reaction between hemoglobin and elevated glucose levels in the blood. It is included among the so-called advanced glycation end products, of which is the most important for the clinical diagnosis of diabetes mellitus, and it can serve as an alternative to glycemia measurement. Compared to the diagnosis of diabetes mellitus by glycemia, the HbA1c level is less influenced by a short-term problem with diabetes compensation. Mass spectroscopy and chromatographic techniques are among the standard methods of HbA1c level measurement. Compared to glycemia measurement, there is lack of simple methods for diabetes mellitus diagnosis by means of the HbA1c assay using a point-of-care test. This review article is focused on the surveying of facts about HbA1c and its importance in diabetes mellitus diagnosis, and surveying standard methods and new methods suitable for the HbA1c assay under point-of-care conditions. Various bioassays and biosensors are mentioned and their specifications are discussed.

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Non-Invasive Blood Glucose Monitoring Technology: A Review

Cited by 226 publications

References 146 publications

Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only

Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only

Continuous Glucose Monitoring in the Hospital

Glycated Hemoglobin and Methods for Its Point of Care Testing

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