Towards non-invasive blood glucose measurement using machine learning: An all-purpose PPG system design

Abstract

Diabetes, the result of excessive or uncontrolled glucose in the blood, is one of the leading causes of human mortality. Due to the unavailability of non-invasive glucose level checker until now, the most trustworthy day-to-day life glucose test for personal healthcare is the use of glucometer in which case painful finger pricking is an obvious part. However, researches have been done to prove the usage of pulse oximeter to measure the blood glucose level besides other physiological indicators such as heart rate, percentage of blood oxygen, etc. Here, as the first of two studies, we try to develop an all-purpose commercial prototype photoplethysmography (PPG) system to monitor necessary health indicator parameters in a non-invasive way. The developed fingertip PPG device consists of both transmissive and reflective type data acquisition system after illuminating the skin with red, green, and IR LEDs. Next, as the second study, special consideration is given to prove the efficiency of the device for measuring blood glucose level (BGL). To measure blood glucose from PPG signal, a few discriminative and related features are extracted from the obtained PPG signals. Machine learning algorithms are employed to predict the actual value of BGL from the extracted features. The proposed algorithm and system can predict the BGL level with a level of clinical accuracy. In the Clarke error grid plot, 96.15% and 3.85% of data are in the zone A and zone B, respectively, with 0% data in the critical zones.