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Review
. 2022 Jun 27;22(13):4855.
doi: 10.3390/s22134855.

Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology-A Review

Aminah Hina  1 , Wala Saadeh  1
Affiliations
Review

Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology-A Review

Aminah Hina et al. Sensors (Basel). .

Abstract

The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with machine learning methods are discussed. The review concludes with key points and insights for future development of PPG NIR-based blood glucose monitoring systems.

Keywords: Photoplethysmography (PPG); machine learning (ML) methods; near-infrared (NIR); noninvasive glucose monitoring.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Noninvasive glucose monitoring system.
Figure 2
Figure 2
Infrared spectroscopy.
Figure 3
Figure 3
Thermal Emission Spectroscopy.
Figure 4
Figure 4
Microwave Spectroscopy working principle.
Figure 5
Figure 5
A prototype for the NIR transmission spectroscopy using a 940 nm wavelength for a noninvasive glucose monitoring system.
Figure 6
Figure 6
Block diagram of the NIR PPG signal glucose sensing platform with machine learning.
Figure 7
Figure 7
PPG waveform and its basic features.
Figure 8
Figure 8
Measured PPG signals with BGL estimation for 3 different subjects having reference a BGL of 79, 115, and 318 mg/dL, respectively. Reprinted with permission from [44].
Figure 9
Figure 9
The Clarke error grid analysis of estimated and reference BGL. Reprinted with permission from [44].
See this image and copyright information in PMC

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