@inproceedings{63d7eb025ba4446497926e6b65253031,
title = "Review of Non-Invasive Blood Glucose Level Estimation based on Photoplethysmography and Artificial Intelligent Technology",
abstract = "The emergence of photoplethysmography for the non-invasive estimation of blood glucose levels in diabetes management offers an alternative solution to the limitations of invasive methods. The application of artificial intelligence technology to PPG signals for non-invasive measurement of monitoring blood glucose level (BGL) using either a machine learning (ML) or deep learning (DL) approach is proven to improve the resulting performance. This review is presented to provide concise information about current and proposed technologies developments of non-invasive blood glucose level monitoring methods using photoplethysmography. The study focuses on the opportunities and constraints in developing research on this topic.",
keywords = "artificially intelligence, blood glucose level, deep learning, estimation, machine learning, non-invasive, photoplethysmography, PPG signal",
author = "Ernia Susana and Kalamullah Ramli",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering ; Conference date: 13-10-2021 Through 15-10-2021",
year = "2021",
doi = "10.1109/QIR54354.2021.9716164",
language = "English",
series = "17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "158--163",
booktitle = "17th International Conference on Quality in Research, QIR 2021",
address = "United States",
}
