Ballistocardiography Signal Quality Assessment Using Machine Learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260462" target="_blank" >RIV/61989100:27240/25:10260462 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11253571" target="_blank" >https://ieeexplore.ieee.org/document/11253571</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EMBC58623.2025.11253571" target="_blank" >10.1109/EMBC58623.2025.11253571</a>
Alternative languages
Result language
angličtina
Original language name
Ballistocardiography Signal Quality Assessment Using Machine Learning
Original language description
Ballistocardiography (BCG) is a non-invasive and versatile technique for detecting cardiac activity. Its flexibility makes it suitable for use in everyday and clinical settings, and it has also demonstrated promise for application in magnetic resonance imaging (MRI) gating. The paper focuses on the automatic identification of the BCG signal quality, which aims to increase the heartbeat detection accuracy, crucial for precise MR triggering. We analyzed 21 signal features based on their statistical properties and evaluated 14 machine learning models to classify BCG segments as high- or low-quality. Results showed the best performance of 4 ensemble classifiers, with the the Extra Trees Classifier achieving the highest overall accuracy of 86.65%, sensitivity of 80.54%, and precision of 73.62%. We also examined feature importance scores, finding that time-domain features generally outperformed those in the frequency domain. These insights can help guide future research and improve the robustness of BCG-based applications.Clinical relevance - This BCG signal-processing method was developed specifically for multi-channel sensory systems to automate channel selection rather than relying on visual assessment. By incorporating this technique into cardiac MRI examinations, we could improve heartbeat detection accuracy, reduce staff workload, and streamline acquisition of MRI data, which is often time-consuming and challenging in terms of image quality lacking motion or other artifacts due to insufficient gating.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
—
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
Article name in the collection
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS
ISBN
979-8-3315-8619-5
ISSN
2375-7477
e-ISSN
2694-0604
Number of pages
7
Pages from-to
1-7
Publisher name
1
Place of publication
1
Event location
Copenhagen, Denmark
Event date
Jul 14, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—