Predictive Obstetrics: Electrohysterogram-Based Detection of Preterm Labor
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260474" target="_blank" >RIV/61989100:27240/25:10260474 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11252962" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11252962</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EMBC58623.2025.11252962" target="_blank" >10.1109/EMBC58623.2025.11252962</a>
Alternative languages
Result language
angličtina
Original language name
Predictive Obstetrics: Electrohysterogram-Based Detection of Preterm Labor
Original language description
Preterm birth is a significant cause of perinatal morbidity and mortality, where early and reliable risk prediction can substantially reduce complications and improve maternal and neonatal health outcomes. Electrohysterography (EHG) emerges as a suitable non-invasive technique for monitoring uterine activity and detecting preterm labor, offering higher accuracy and robustness against noise compared to traditional methods. This study focuses on the classification of term and preterm records using three machine learning algorithms: decision trees, subspace k-nearest neighbors, and a trilayered neural network. The performance of these algorithms is evaluated using the evaluation metrics of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1-score. Results showed that the highest classification accuracy was achieved with decision trees, both on the original imbalanced dataset (ACC = 85.56%, SE = 87.01%, PPV = 98.09%, F1 = 92.22%) and on the dataset balanced using the synthetic minority oversampling technique (ACC = 69.44%, SE = 85.42%, PPV = 78.34%, F1 = 81.73%). However, it was observed that all algorithms struggled to classify the minority class, and results on synthetically balanced data were lower, likely due to the poor quality of the generated data.Clinical relevance - The use of an alternative EHG monitoring technique combined with machine learning can significantly support obstetricians in daily clinical practice, improve the prediction of preterm labor, and minimize complications for both the mother and the fetus.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20201 - Electrical and electronic 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
Article name in the collection
2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC : Proceedings
ISBN
979-8-3315-8619-5
ISSN
2375-7477
e-ISSN
2694-0604
Number of pages
7
Pages from-to
1-7
Publisher name
IEEE
Place of publication
Piscataway
Event location
Kodaň
Event date
Jul 14, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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