NON-INVASIVE PPG-BASED ESTIMATION OF BLOOD GLUCOSE LEVEL

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F23%3A00133826" target="_blank" >RIV/00216224:14110/23:00133826 - isvavai.cz</a>

Výsledek na webu

<a href="https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/9454" target="_blank" >https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/9454</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/CTJ.2023.1.04" target="_blank" >10.14311/CTJ.2023.1.04</a>

Jazyk výsledku

angličtina

Název v původním jazyce

NON-INVASIVE PPG-BASED ESTIMATION OF BLOOD GLUCOSE LEVEL

Popis výsledku v původním jazyce

This paper focuses on non-invasive blood glucose determination using photoplethysmographic (PPG) signals, which is crucial for managing diabetes. Diabetes stands as one of the world’s major chronic diseases. Untreated diabetes frequently leads to fatalities. Current self-monitoring techniques for measuring diabetes require invasive procedures such as blood or bodily fluid sampling, which may be very uncomfortable. Hence, there is an opportunity for non-invasive blood glucose monitoring through smart devices capable of measuring PPG signals. The primary goal of this research was to propose methods for glycemic classification into two groups (low and high glycemia) and to predict specific glycemia values using machine learning techniques. Two datasets were created by measuring PPG signals from 16 individuals using two different smart devices – a smart wristband and a smartphone. Simultaneously, the reference blood glucose levels were invasively measured using a glucometer. The PPG signals were preprocessed, and 27 different features were extracted. With the use of feature selection, only 10 relevant features were chosen. Numerous machine learning models were developed. Random Forest (RF) and Support Vector Machine (SVM) with the radial basis function (RBF) kernel performed best in classifying PPG signals into two groups. These models achieved an accuracy of 76% (SVM) and 75% (RF) on the smart wristband test dataset. The functionality of the proposed models was then verified on the smartphone test dataset, where both models achieved similar accuracy: 74% (SVM) and 75% (RF). For predicting specific glycemia values, RF performed best. Mean Absolute Error (MAE) was 1.25 mmol/l on the smart wristband test dataset and 1.37 mmol/l on the smartphone test dataset.

Název v anglickém jazyce

NON-INVASIVE PPG-BASED ESTIMATION OF BLOOD GLUCOSE LEVEL

Popis výsledku anglicky

This paper focuses on non-invasive blood glucose determination using photoplethysmographic (PPG) signals, which is crucial for managing diabetes. Diabetes stands as one of the world’s major chronic diseases. Untreated diabetes frequently leads to fatalities. Current self-monitoring techniques for measuring diabetes require invasive procedures such as blood or bodily fluid sampling, which may be very uncomfortable. Hence, there is an opportunity for non-invasive blood glucose monitoring through smart devices capable of measuring PPG signals. The primary goal of this research was to propose methods for glycemic classification into two groups (low and high glycemia) and to predict specific glycemia values using machine learning techniques. Two datasets were created by measuring PPG signals from 16 individuals using two different smart devices – a smart wristband and a smartphone. Simultaneously, the reference blood glucose levels were invasively measured using a glucometer. The PPG signals were preprocessed, and 27 different features were extracted. With the use of feature selection, only 10 relevant features were chosen. Numerous machine learning models were developed. Random Forest (RF) and Support Vector Machine (SVM) with the radial basis function (RBF) kernel performed best in classifying PPG signals into two groups. These models achieved an accuracy of 76% (SVM) and 75% (RF) on the smart wristband test dataset. The functionality of the proposed models was then verified on the smartphone test dataset, where both models achieved similar accuracy: 74% (SVM) and 75% (RF). For predicting specific glycemia values, RF performed best. Mean Absolute Error (MAE) was 1.25 mmol/l on the smart wristband test dataset and 1.37 mmol/l on the smartphone test dataset.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

30105 - Physiology (including cytology)

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 periodika

Lékař a technika

ISSN

0301-5491

e-ISSN

—

Svazek periodika

53

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

19-24

Kód UT WoS článku

—

EID výsledku v databázi Scopus

999