Improved systolic peak detection in photoplethysmography signals: focus on atrial fibrillation
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Improved systolic peak detection in photoplethysmography signals: focus on atrial fibrillation
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
20601 - Medical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Lékař a technika
ISSN
0301-5491
e-ISSN
2336-5552
Volume of the periodical
54
Issue of the periodical within the volume
4
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
4
Pages from-to
1-4
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105001101541