A Systematic Review of Noninvasive Blood Glucose Estimation Using Near Infrared
Keywords:
Blood Glucose, Machine Learning, Near Infrared, Noninvasive,Abstract
Diabetes is a chronic and lifelong disease, one of the ten highest causes of death in the world. Diabetes management can only be done by carrying out continuous monitoring. Noninvasive blood glucose measuring devices are needed to overcome the weaknesses of invasive methods, but their accuracy still needs to be improved. This review aims to identify factors that influence the accuracy of estimating blood glucose levels using noninvasive methods based on NIR signals and to observe the development of this technology over the last five years. We performed a systematic review based on articles focusing on noninvasive blood glucose level estimation using near-infrared. This systematic review used the PRISMA 2020 guidelines. Primary studies were retrieved from the literature search engine Scopus database, including journals and proceedings: IEEE, Science Direct, Springer Link, MDPI, Word Scientific, and others. This review provides an overview of using NIR and PPG signals, primary and advanced signal processing, conventional and machine learning approaches, and trends. A total of 62 studies were included. Thirty studies used the conventional approach, and thirty-two studies used machine learning. Thirty-eight studies use primary signal processing, and twenty-four studies use advanced signal processing. Forty studies use NIR signals, and twenty-two studies use PPG signals. India, China, and Indonesia are the top 3 countries in publications on this topic. Using advanced signal processing and feature extraction on photoplethysmography signals and machine learning as an estimation method is quite promising for increasing accuracy. The best machine learning method can be analyzed using meta-analysis.
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