A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography
Abstract
Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created the necessity for continuous noninvasive glucose monitoring. This article presents a wearable system for glucose monitoring based on a single wavelength near-infrared (NIR) Photoplethysmography (PPG) combined with machine-learning regression (MLR). The PPG readout circuit consists of a switched capacitor Transimpedance amplifier with 1 MΩ gain and a 10-Hz switched capacitor LPF. It allows a DC bias current rejection up to 20 μA with an input-referred current noise of 7.3 pA/√Hz. The proposed digital processor eliminates motion artifacts, and baseline drifts from PPG signal, extracts six distinct features and finally predicts the blood glucose level using Support Vector Regression with Fine Gaussian kernel (FGSVR) MLR. A novel piece-wise linear (PWL) approach for the exponential function is proposed to realize the FGSVR on-chip. The overall system is implemented using a 180 nm CMOS process with a chip area of 4.0 mm2while consuming 1.62 mW. The glucose measurements are performed for 200 subjects with R2of 0.937. The proposed system accurately predicts the sugar level with a mean absolute relative difference (mARD) of 7.62%.
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Blood glucose level has been estimated from PPG pulse wave shape.
“Blood glucose level, widely used for diabetes self-management, has been estimated from PPG pulse wave shape”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [176]
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Wearable Photoplethysmography for Cardiovascular Monitoring
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- Authors
- Hina, A., & Saadeh, W.
- Affiliations
- Lahore University of Management Sciences (PK)
- Venue
- IEEE Transactions on Biomedical Circuits and Systems
- Published
- 2020-06
- Volume
- 14
- Issue
- 3
- Pages
- 504-515
- ISSN
- 1932-4545 (ISSN-L)
- 1940-9990
- OpenAlex
- W3011056083
- Cited by
- 99
- References
- 53
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 17 · 2023: 19 · 2022: 21 · 2021: 12 · 2020: 7
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Biomedical Circuits and Systems (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Higher Education Commission (7982/Punjab/NRPU/R&D/HEC/2017)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Analog and Mixed-Signal Circuit Design
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Keywords
- non-invasive glucose monitoring, photoplethysmography, near-infrared, wearable sensors, motion artifact removal, baseline drift removal, support vector regression, piecewise linear approximation, diabetes mellitus, CMOS integrated circuit
- MeSH terms
- Machine Learning, Wearable Electronic Devices, Adolescent, Adult, Aged, Algorithms, Amplifiers, Electronic, Blood Glucose, Equipment Design, Female, Humans, Male, Middle Aged, Monitoring, Physiologic, Signal Processing, Computer-Assisted, Photoplethysmography, Young Adult, Lab-On-A-Chip Devices
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