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.

Authors with CRIS profile

Katharina Jäger Machine Learning and Data Analytics Lab Michael Nissen Machine Learning and Data Analytics Lab Robert Richer Machine Learning and Data Analytics Lab Simone Rahm Machine Learning and Data Analytics Lab Peter Fasching Professur für Translationale Frauenheilkunde und Geburtshilfe Björn Eskofier Machine Learning and Data Analytics Lab Heike Leutheuser Machine Learning and Data Analytics Lab

Related research project(s)

SMART Start: Smarte Sensorik in der Schwangerschaft - Ein integratives Konzept zur digitalen, präventiven Versorgung schwangerer Frauen (SMART Start) March 1, 2020 - Jan. 31, 2024

How to cite

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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