Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG

Jäger K, Nissen M, Richer R, Rahm S, Titzmann A, Fasching P, Eskofier B, Leutheuser H (2022)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Original Authors: Katharina M. Jaeger, Michael Nissen, Robert Richer, Simone Rahm, Adriana Titzmann, Peter A. Fasching, Bjoern M. Eskofier, Heike Leutheuser

Event location: Ioannina GR

DOI: 10.1109/BHI56158.2022.9926804

Abstract

Preterm births account for more than 10 % of all newborns. An adverse fetal presentation is a risk factor for intrapartum and neonatal mortality. To date, no technology enables a longitudinal, ubiquitous, and unobtrusive monitoring of fetal presentation. This study presents a first approach to fetal orientation detection based on non-invasive fetal electrocardiography (NI-fECG) using the non-invasive multi-modal foetal ECG-Doppler data set for antenatal cardiology research. The data set contains 60 recordings from 39 pregnant women (21–27 weeks), including NI-fECG and ultrasound position ground truth. We evaluated both handcrafted and generic features for five different classifiers (k-Nearest-Neighbor, Decision Tree Classifier, Support Vector Classification, AdaBoost Classifier, and Multilayer Perceptron) using cross-validation on subject splits on a cleaned subset. Best results for the distinction between vertex (head down) and breech (head up) were achieved using an AdaBoost classifier with a balanced accuracy of 86.5 ± 15.0 %. With this work, we take a first step towards longitudinal fetal presentation monitoring, which contributes to a better understanding of reduced fetal movements and extends the potential applications of NI-fECG in prenatal care. In future work, we will expand our classification system to detect more detailed fetal presentations using a newly created data set.

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APA:

Jäger, K., Nissen, M., Richer, R., Rahm, S., Titzmann, A., Fasching, P.,... Leutheuser, H. (2022). Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG. In IEEE (Eds.), Proceedings of the 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). Ioannina, GR.

MLA:

Jäger, Katharina, et al. "Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG." Proceedings of the 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina Ed. IEEE, 2022.

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