Imperial College London
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DrBennyLo

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Visiting Reader
 
 
 
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Contact

 

+44 (0)20 7594 0806benny.lo Website

 
 
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Location

 

Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zhang:2020:10.1109/TII.2020.2975222,
author = {Zhang, G and Mei, Z and Zhang, Y and Ma, X and Lo, B and Chen, D and Zhang, Y},
doi = {10.1109/TII.2020.2975222},
journal = {IEEE Transactions on Industrial Informatics},
pages = {7209--7218},
title = {A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning},
url = {http://dx.doi.org/10.1109/TII.2020.2975222},
volume = {16},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Blood glucose level needs to be monitored regularly to manage the health condition of hyperglycemic patients. The current glucose measurement approaches still rely on invasive techniques which are uncomfortable and raise the risk of infection. To facilitate daily care at home, in this article, we propose an intelligent, noninvasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline, and warning based on smartphone photoplethysmography (PPG) signals. The main implementation processes of the proposed system include 1) a novel algorithm for acquiring PPG signals using only smartphone camera videos; 2) a fitting-based sliding window algorithm to remove varying degrees of baseline drifts and segment the signal into single periods; 3) extracting characteristic features from the Gaussian functions by comparing PPG signals at different blood glucose levels; 4) categorizing the valid samples into three glucose levels by applying machine learning algorithms. Our proposed system was evaluated on a data set of 80 subjects. Experimental results demonstrate that the system can separate valid signals from invalid ones at an accuracy of 97.54% and the overall accuracy of estimating the blood glucose levels reaches 81.49%. The proposed system provides a reference for the introduction of noninvasive blood glucose technology into daily or clinical applications. This article also indicates that smartphone-based PPG signals have great potential to assess an individual's blood glucose level.
AU  - Zhang,G
AU  - Mei,Z
AU  - Zhang,Y
AU  - Ma,X
AU  - Lo,B
AU  - Chen,D
AU  - Zhang,Y
DO  - 10.1109/TII.2020.2975222
EP  - 7218
PY  - 2020///
SN  - 1551-3203
SP  - 7209
TI  - A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning
T2  - IEEE Transactions on Industrial Informatics
UR  - http://dx.doi.org/10.1109/TII.2020.2975222
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000554904700047&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://ieeexplore.ieee.org/document/9005207
UR  - http://hdl.handle.net/10044/1/88074
VL  - 16
ER  -
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