In-Vivo Estimation of Glucose Level Using PPG Signal
Abstract
Despite being at the start of the 21st-century, the non-invasive measurement of blood glucose still remains a challenging task in the scientific world. In recent times, several studies have been carried out based on the optical properties of light to measure the changes in blood volume and tried to calculate various physiological parameters including blood glucose using PPG signal. In our study, we try to develop a non-invasive method for blood glucose estimation by transmissive and reflective PPG signals of the subjects. We also develop a hardware system to collect PPG. In our proposed method, we accurately relate the features that are obtained from PPG to the original blood glucose values from a glucometer. Our proposed methodology has maximum correlation value of 0.91.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Researchers estimated blood glucose levels from green and red light on fingers using machine learning and random forest regression.
“The researchers tried to estimate blood glucose levels [91] from green and red light on fingers using machine learning and a random forest regression algorithm.”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [91]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Welch, P. D. (1967) · IEEE Transactions on Audio and Electroacoustics · doi:10.1109/tau.1967.1161901 · Cited by 12,363 · see citing works
Multiparameter Respiratory Rate Estimation From the Photoplethysmogram
Karlen, W., Raman, S., Ansermino, J. M., & Dumont, G. A. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2246160 · Cited by 486 · see citing works
Some useful properties of Teager's energy operators
Kaiser, J. F. (1993) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1993.319457 · Cited by 347 · see citing works
Monte-Moreno, E. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.05.001 · Cited by 311 · see citing works
Robust endpoint detection and energy normalization for real-time speech and speaker recognition
Li, Q. L., Zheng, J., Tsai, A., et al. (2002) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2002.1001979 · Cited by 200 · see citing works
Hartmann, V., Liu, H., Chen, F., Qiu, Q., Hughes, S., & Zheng, D. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00198 · Cited by 173 · see citing works
Chen, C. H. (1988) · Medical Entomology and Zoology · Cited by 140 · see citing works
Gušev, M., Poposka, L., Spasevski, G., Kostoska, M., Koteska, B., Simjanoska, M., Ackovska, N., Stojmenski, A., Tasič, J. F., & Trontelj, J. K. (2020) · Journal of Sensors · doi:10.1155/2020/9628281 · Cited by 84 · see citing works
Design and development of non invasive glucose measurement system
Paul, B., Manuel, M. P., & Alex, Z. C. (2012) · International Symposium on Physics and Technology of Sensors · doi:10.1109/ispts.2012.6260873 · Cited by 63 · see citing works
Noninvasive exploration of cardiac autonomic neuropathy. Four reliable methods for diabetes?
Ducher, M., Cerutti, C., Gustin, M.-P., et al. (1999) · Diabetes Care · doi:10.2337/diacare.22.3.388 · Cited by 53 · see citing works
Non-invasive blood glucose monitor based on spectroscopy using a smartphone
Dantu, V., Vempati, J., & Srivilliputhur, S. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944425 · Cited by 44 · see citing works
Investigations on Multisensor-Based Noninvasive Blood Glucose Measurement System
Yadav, J., Rani, A., Singh, V., & Murari, B. M. (2017) · Journal of Medical Devices · doi:10.1115/1.4036580 · Cited by 38 · see citing works
Non-invasive blood glucose detection system based on conservation of energy method
Zhang, Y., Zhu, J., Liang, Y., Chen, H., Yin, S.-M., & Chen, Z. (2017) · Physiological Measurement · doi:10.1088/1361-6579/aa50cf · Cited by 32 · see citing works
Photoplethysmography: Analysis of the Pulse Oximeter Waveform
Alian, A. A., & Shelley, K. H. (2013) · Springer eBooks · doi:10.1007/978-1-4614-8557-5_19 · Cited by 30 · see citing works
In vivo noninvasive blood glucose detection using near-infrared spectrum based on the PSO-2ANN model
Dai, J., Ji, Z., Du, Y., et al. (2018) · Technology and Health Care · doi:10.3233/thc-174592 · Cited by 23 · see citing works
Quantifying tissue mechanical properties using photoplethysmography
Akl, T. J., Wilson, M. A., Ericson, M. N., et al. (2014) · Biomedical Optics Express · doi:10.1364/boe.5.002362 · Cited by 20 · see citing works
Noninvasive glucose evaluation by human skin oxygen saturation level
Wei, T., Tsai, H., Yang, C., et al. (2016) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2016.7520571 · Cited by 11 · see citing works
Liu, W., Huang, A., & Wang, P. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217690 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Review of Wearable Multi-Wavelength Photoplethysmography
Ray, D., Collins, T., Woolley, S., & Ponnapalli, P. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3121476 · Cited by 155 · see citing works
The Current State of Optical Sensors in Medical Wearables
Vavrinsky, E., Esfahani, N. E., Hausner, M., Kuzma, A., Rezo, V., Donoval, M., & Kosnacova, H. (2022) · Biosensors · doi:10.3390/bios12040217 · Cited by 103 · see citing works
“The researchers tried to estimate blood glucose levels [91] from green and red light on fingers using machine learning and a random forest regression algorithm.”
Method
- Authors
- Gupta, S. S., Hossain, S., Haque, C. A., & Kim, K.-D.
- Affiliations
- Kookmin University (KR)
- Venue
- 2020 International Conference on Information and Communication Technology Convergence (ICTC)
- Publisher
- IEEE
- Published
- 2020-10-21
- Pages
- 733-736
- OpenAlex
- W3116534403
- Cited by
- 12
- References
- 19
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 2 · 2022: 4 · 2021: 2
- FWCI
- 3.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Information and Communication Technology Convergence (ICTC) (publishedVersion)
- Funders
- Ministry of Education
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Keywords
- blood glucose estimation, photoplethysmography, non-invasive glucose monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
