Estimation of blood glucose level based on PPG signals measured by smart devices

conference paper

English

This paper deals with the possibilities of non-invasive determination of blood glucose from photoplethysmographic signals. Monitoring blood sugar is the most important part of managing diabetes. Diabetes is one of the world’s major chronic diseases. Untreated diabetes is often a cause of death. Two datasets have been created by recording the photoplethysmographic signals of 16 people using two smart devices (a smart wristband and a smartphone), along with their blood glucose levels measured in an invasive way. The photoplethysmographic signals were preprocessed, and suitable features were extracted from them. The aim of the work is to propose methods for glycemic classification and prediction. Various machine-learning models were created. The best model for classifying blood glucose into two groups (low blood glucose and high blood glucose) is random forest, which achieves an F1 score of 84% and 80% on two different test sets obtained from two smart devices. The best blood glucose level prediction model is also based on random forest and achieves an MAE of 1.02 mmol/l and 1.17 mmol/l on both testing datasets.

PPG; glycemia; diabetes; smartphone; smart devices; classification; prediction

VARGOVÁ, E.; NĚMCOVÁ, A.

25. 4. 2023

Brno University of Technology, Faculty of Elektronic Engineering and Communication

Brno

978-80-214-6154-3

Proceedings II of the 29 th Conference STUDENT EEICT 2023 Selected papers

1st Edition

2788-1334

Proceedings II of the Conference STUDENT EEICT

Czech Republic

137

140

4

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf