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

Abstract

Blood glucose needs to be monitored on a regular basis to prevent diabetes from consuming the health of hyperglycemic patients. Currently in clinic, it is measured using an invasive technique which is uncomfortable and has risky of infection. To facilitate daily care at home, we propose an intelligent, non-invasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline and warning based on smartphone PPG signals. The main implementation processes of the proposed system include: (1) a novel algorithm for acquiring photoplethysmography (PPG) signals using only smartphone camera videos; (2) a fitting-based sliding window (FSW) 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 non-invasive blood glucose technology into daily or clinical applications. This research also indicates that smartphone-based PPG signals have great potentiality to assess individual's blood glucose level.

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