IEEE Trans Biomed Circuits Syst. 2020 Jun;14(3):504-515. doi: 10.1109/TBCAS.2020.2979514. Epub 2020 Mar 9.
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 while consuming 1.62 mW. The glucose measurements are performed for 200 subjects with R of 0.937. The proposed system accurately predicts the sugar level with a mean absolute relative difference (mARD) of 7.62%.
用于监测全球日益增多的糖尿病患者的传统血糖监测方法具有侵入性、痛苦、昂贵且耗时。糖尿病患者血糖水平异常引起的并发症，促使人们需要进行连续的非侵入性血糖监测。本文提出了一种基于单波长近红外（NIR）光体积描记术（PPG）与机器学习回归（MLR）相结合的可穿戴血糖监测系统。PPG 读取电路由一个具有 1MΩ增益和 10Hz 开关电容 LPF 的开关电容跨阻放大器组成。它允许以 20μA 的直流偏置电流抑制，输入参考电流噪声为 7.3pA/√Hz。所提出的数字处理器可消除 PPG 信号中的运动伪影和基线漂移，提取六个独特的特征，并最终使用支持向量回归与精细高斯核（FGSVR）MLR 预测血糖水平。本文提出了一种新颖的分段线性（PWL）方法来实现片上的 FGSVR。该系统采用 180nm CMOS 工艺实现，芯片面积为 4.0mm²，功耗为 1.62mW。对 200 名受试者进行了血糖测量，R 值为 0.937。该系统可准确预测血糖水平，平均绝对相对差异（mARD）为 7.62%。
