A 186μW Photoplethysmography-Based Noninvasive Glucose Sensing SoC
Aminah Hina, Wala Saadeh
Abstract
Recent trends in research and the market show high demand for frequent monitoring of glucose to estimate the blood sugar level non-invasively which can replace the conventional finger-prick glucometer for daily use. This paper presents a high precision near-infrared Photoplethysmography (PPG) based noninvasive glucose monitoring System on Chip (SoC). The proposed system implements a fully differential Analog Frontend (AFE) with nonlinear medium Gaussian support-vector-regression (NMG-SVR) for glucose estimation. The AFE design incorporates chopping which enables the reduction of the integrated input-referred current noise to 9.4pArms thus achieving a dynamic range of 115dB. The glucose prediction processor (GPP) removes noise from the PPG signal, extracts ten unique features, and estimates the blood glucose level using a trained customized NMG-SVR model that minimizes the hardware cost by 25%. The extracted features are carefully designed and implemented to ensure inter-feature dependency, which helps to reduce the overall area by more than 40%. Moreover, GPP is implemented using power and clock gating techniques to minimize both static and dynamic power consumption. The proposed SoC is realized with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS technology and occupies an area of 6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . It dissipates a power of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$186~\mu \text{W}$ </tex-math></inline-formula> and achieves a mean absolute relative difference (mARD) of 6.9% verified on 200 subjects.