A $186\mu \mathrm{W}$ Glucose Monitoring SoC using Near-Infrared Photoplethysmography

Hina, Aminah; Saadeh, Wala

doi:10.1109/a-sscc48613.2020.9336124

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…Furthermore, the early stopping method avoided over-training of the model. In comparison with [12,13,[15][16][17]21,[25][26][27], which served as the standard for predicting blood glucose, the proposed blood glucose estimation system had an MSE, RMSE, MAE, MARD, and R 2 of 40.736, 6.3824, 5.0896, 4.4321, and 0.997, respectively, all of which fell within region A of the Clarke EGA.…”

Section: Analyses and Discussion Of Network Experiments Resultsmentioning

confidence: 99%

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

Yen

Chen

Wang

et al. 2022

Sensors

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This study proposed a noninvasive blood glucose estimation system based on dual-wavelength photoplethysmography (PPG) and bioelectrical impedance measuring technology that can avoid the discomfort created by conventional invasive blood glucose measurement methods while accurately estimating blood glucose. The measured PPG signals are converted into mean, variance, skewness, kurtosis, standard deviation, and information entropy. The data obtained by bioelectrical impedance measuring consist of the real part, imaginary part, phase, and amplitude size of 11 types of frequencies, which are converted into features through principal component analyses. After combining the input of seven physiological features, the blood glucose value is finally obtained as the input of the back-propagation neural network (BPNN). To confirm the robustness of the system operation, this study collected data from 40 volunteers and established a database. From the experimental results, the system has a mean squared error of 40.736, a root mean squared error of 6.3824, a mean absolute error of 5.0896, a mean absolute relative difference of 4.4321%, and a coefficient of determination (R Squared, R2) of 0.997, all of which fall within the clinically accurate region A in the Clarke error grid analyses.

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Section: Analyses and Discussion Of Network Experiments Resultsmentioning

confidence: 99%

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

Yen

Chen

Wang

et al. 2022

Sensors

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“…It achieves an mARD of 7.62 and an R 2 of 0.937. The authors [44][45][46] also use a similar feature set as [38] for glucose estimation. PPG signals of 1 min are recorded from diabetic and non-diabetic subjects.…”

Section: Nir Ppg Signal Analysis With Machine Learningmentioning

confidence: 99%

“…This section briefly describes the mathematics of the features extracted from PPG signals in thework [44][45][46][47].…”

Section: Mathematical Details Of Ppg Featuresmentioning

confidence: 99%

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

Hina

Saadeh

2022

Sensors

Self Cite

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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.

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“…To achieve monotonicity, a unary DAC is preferred for the proposed application. Therefore, the DAC has 2 6 parallel MOS current sources along with switches to control the current through them. Current sources are realized using two NMOS (W/L = 2um/180nm) in series, as shown in Fig.…”

Section: B) Glitch Free Current Steering Dacmentioning

confidence: 99%

A 77.8-dB DR, 114.2 fJ/Step Amplifier-Less Current-to-Digital Converter

Ayub

Altaf

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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A charge-recycled amplifier-less current-to-digital converter (CDC) is presented in this paper. The highly digital CDC is based on ultra-low-power subthreshold operation, amplifier-free design, scalable current range, and high-resolution digital circuits. It is realized using a mixed-signal approach by utilizing an analog comparator, 6-bits current steering DAC, an implicit passive integrator, and digital signal conditioning circuits. The proposed converter can be operated in tri-modes to support multiple current sensing applications, i.e. Mode1: current range (CR) from 10nA to 400nA, Mode 2: CR from 0.5μA to 5μA, and Mode3: CR from 2.5μA to 42.5μA. The proposed CDC is implemented in 180nm CMOS process with an active area of 0.54 mm 2 while consuming 11.2µW. It achieves a 77.8dB dynamic range at a bandwidth of 300Hz from 1V supply with an FoM of 114.2fJ/step.

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A $186\mu \mathrm{W}$ Glucose Monitoring SoC using Near-Infrared Photoplethysmography

Cited by 9 publications

References 9 publications

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

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

A 77.8-dB DR, 114.2 fJ/Step Amplifier-Less Current-to-Digital Converter

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