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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU148761" target="_blank" >RIV/00216305:26220/23:PU148761 - isvavai.cz</a>
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.13164/eeict.2023.137" target="_blank" >10.13164/eeict.2023.137</a>

Result language
angličtina
Original language name
Estimation of blood glucose level based on PPG signals measured by smart devices
Original language description
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.
Czech name
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Czech description
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Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings II of the 29 th Conference STUDENT EEICT 2023 Selected papers
ISBN
978-80-214-6154-3
ISSN
2788-1334
e-ISSN
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Number of pages
4
Pages from-to
137-140
Publisher name
Brno University of Technology, Faculty of Elektronic Engineering and Communication
Place of publication
Brno
Event location
Brno
Event date
Apr 25, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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