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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

978-80-214-6154-3

ISSN

2788-1334

e-ISSN

—

Počet stran výsledku

4

Strana od-do

137-140

Název nakladatele

Brno University of Technology, Faculty of Elektronic Engineering and Communication

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

25. 4. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—