Department of Electrical Engineering, Lahore University of Management and Sciences, Lahore 54792, Pakistan.
Sensors (Basel). 2022 Jun 27;22(13):4855. doi: 10.3390/s22134855.
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.
过去几十年见证了不断发展的非侵入性连续血糖监测(CGM)系统,这些系统能够准确测量血糖水平。传统的指尖采血方法虽然准确,但由于疼痛和测试条昂贵,每天多次使用并不可行。尽管已经有微创和非侵入性的 CGM 系统进入市场,但它们价格昂贵,并且需要指尖校准。由于糖尿病在低收入和中等收入国家的发病率很高,因此需要一种经济实惠且易于使用的非侵入性血糖监测设备。本文综述简要讨论了非侵入性血糖测量技术及其相关研究工作。讨论的技术包括光学、经皮和酶学。本文重点介绍了近红外(NIR)技术和近红外光体积描记法(PPG)用于血糖预测。讨论了从 PPG 信号中提取特征以及使用机器学习方法进行血糖预测。本文综述最后提出了基于 PPG NIR 的血糖监测系统未来发展的要点和见解。