A Noninvasive Blood Glucose Monitoring System Based on Smartphone PPG Signal Processing and Machine Learning

• Published in: IEEE transactions on industrial informatics Vol. 16; no. 11; pp. 7209 - 7218
• Main Authors: Zhang, Gaobo, Mei, Zhen, Zhang, Yuan, Ma, Xuesheng, Lo, Benny, Chen, Dongyi, Zhang, Yuanting
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biomedical monitoring; Blood; Daily care; Feature extraction; gaussian fitting; Glucose; Glucose monitoring; healthcare based on machine learning; Informatics; Machine learning; Machinery condition monitoring; Monitoring; Monitoring systems; noninvasive blood glucose monitoring; Signal processing; smartphone photoplethysmography (PPG) signal; Smartphones; Sugar; Videos
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.2975222

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<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula> and the overall accuracy of estimating the blood glucose levels reaches 81.49<inline-formula><tex-math notation="LaTeX">\%</tex-math></inline-formula>. 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.