A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography

• Published in: IEEE transactions on biomedical circuits and systems Vol. 14; no. 3; pp. 504 - 515
• Main Authors: Hina, Aminah, Saadeh, Wala
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Aged; Algorithms; Amplifiers, Electronic; Biomedical monitoring; Blood; Blood Glucose - analysis; Capacitors; Circuits; Clocks; CMOS; Diabetes; Diabetes mellitus; Equipment Design; Exponential functions; Feature extraction; Female; Gain; Glucose; Glucose Monitoring; Humans; Lab-On-A-Chip Devices; Learning algorithms; Light emitting diodes; Machine Learning; machine learning regre-ssion; Male; Microprocessors; Middle Aged; Monitoring; Monitoring methods; Monitoring, Physiologic - instrumentation; near-infrared (NIR); Photoplethysmography (PPG); Photoplethysmography - instrumentation; Signal processing; Signal Processing, Computer-Assisted; Sugar; Support vector machines; Wavelength; wearable devices; Wearable Electronic Devices; Young Adult
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2020.2979514

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 mm 2 while consuming 1.62 mW. The glucose measurements are performed for 200 subjects with R 2 of 0.937. The proposed system accurately predicts the sugar level with a mean absolute relative difference (mARD) of 7.62%.