Improved systolic peak detection in photoplethysmography signals: focus on atrial fibrillation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0191265" target="_blank" >RIV/00216305:26220/26:0191265 - isvavai.cz</a>

Výsledek na webu

<a href="https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/9982" target="_blank" >https://ojs.cvut.cz/ojs/index.php/CTJ/article/view/9982</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/CTJ.2024.4.05" target="_blank" >10.14311/CTJ.2024.4.05</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improved systolic peak detection in photoplethysmography signals: focus on atrial fibrillation

Popis výsledku v původním jazyce

Photoplethysmography (PPG) is a widely recognized non-invasive optical technique for monitoring blood volume changes. Recently, PPG signals have gained prominence in healthcare applications, including the detection of cardiac arrhythmias. Cardiac arrhythmias represent a significant global health challenge, with particular focus on identifying atrial fibrillation (AF), the most prevalent type. Accurate detection of systolic peaks in PPG signals is crucial for arrhythmia detection and for other applications such as heart rate estimation and heart rate variability analysis. Despite the high accuracy of existing beat detection methods in healthy subjects, the performance in the presence of cardiac arrhythmias is lower. This study employs a deep learning method to enhance the detection of systolic peaks in PPG signals, even in the presence of AF. The model was trained on a dataset comprising 2,477 10-second PPG segments with over 37,000 annotated PPG peaks, including data from AF patients. Our model achieved an F1 score of 97.3 % on the test dataset and F1 score of 94.8 % on the test dataset when considering only AF patients.

Název v anglickém jazyce

Improved systolic peak detection in photoplethysmography signals: focus on atrial fibrillation

Popis výsledku anglicky

Photoplethysmography (PPG) is a widely recognized non-invasive optical technique for monitoring blood volume changes. Recently, PPG signals have gained prominence in healthcare applications, including the detection of cardiac arrhythmias. Cardiac arrhythmias represent a significant global health challenge, with particular focus on identifying atrial fibrillation (AF), the most prevalent type. Accurate detection of systolic peaks in PPG signals is crucial for arrhythmia detection and for other applications such as heart rate estimation and heart rate variability analysis. Despite the high accuracy of existing beat detection methods in healthy subjects, the performance in the presence of cardiac arrhythmias is lower. This study employs a deep learning method to enhance the detection of systolic peaks in PPG signals, even in the presence of AF. The model was trained on a dataset comprising 2,477 10-second PPG segments with over 37,000 annotated PPG peaks, including data from AF patients. Our model achieved an F1 score of 97.3 % on the test dataset and F1 score of 94.8 % on the test dataset when considering only AF patients.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

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

2336-5552

Svazek periodika

54

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

4

Strana od-do

1-4

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105001101541