Towards non-invasive blood glucose measurement using machine learning: An all-purpose PPG system design. (July 2021)

• Record Type: Journal Article
• Authors: Sen Gupta, Shantanu; Kwon, Tae-Ho; Hossain, Shifat; Kim, Ki-Doo
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Photoplethysmography -- Biomedical device -- Glucose -- Diabetes -- Features -- Machine learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102706
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A commercial level compact PPG device is developed. Proposed PPG device supports both reflective and transmissive type signal acquisition. The fingertip PPG device utilizes three different wavelengths: green, red, and IR. A machine learning based glucose estimation process is developed and evaluated by using the developed device. 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.