Power-MF: Robust fetal QRS detection from non-invasive fetal electrocardiogram recordings

Jäger K, Nissen M, Rahm S, Titzmann A, Fasching P, Beilner J, Eskofier B, Leutheuser H (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Physiological Measurement Institute of Physics: Hybrid Open Access

Abstract

Objective. Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts. Approach. In this work, we propose Power-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark Power-MF against three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA). Main results. Our results show that Power-MF outperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5 % ± 0.5 % F1-score, B2 Labour: 98.0 % ± 3.0 % F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise. Significance. Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

Authors with CRIS profile

Katharina Jäger Machine Learning and Data Analytics Lab Michael Nissen Machine Learning and Data Analytics Lab Simone Rahm Machine Learning and Data Analytics Lab Adriana Titzmann Professur für Translationale Frauenheilkunde und Geburtshilfe Peter Fasching Professur für Translationale Frauenheilkunde und Geburtshilfe Janina Beilner Machine Learning and Data Analytics Lab 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., Rahm, S., Titzmann, A., Fasching, P., Beilner, J.,... Leutheuser, H. (2024). Power-MF: Robust fetal QRS detection from non-invasive fetal electrocardiogram recordings. Physiological Measurement. https://doi.org/10.1088/1361-6579/ad4952

MLA:

Jäger, Katharina, et al. "Power-MF: Robust fetal QRS detection from non-invasive fetal electrocardiogram recordings." Physiological Measurement (2024).

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