Predictive Obstetrics: Electrohysterogram-Based Detection of Preterm Labor

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Predictive Obstetrics: Electrohysterogram-Based Detection of Preterm Labor

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Predictive Obstetrics: Electrohysterogram-Based Detection of Preterm Labor

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic 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

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

Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Kodaň

Datum konání akce

14. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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