Ballistocardiography Signal Quality Assessment Using Machine Learning

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Ballistocardiography Signal Quality Assessment Using Machine Learning

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Ballistocardiography Signal Quality Assessment Using Machine Learning

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information 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 statě ve sborníku

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

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

1

Místo vydání

1

Místo konání akce

Copenhagen, Denmark

Datum konání akce

14. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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