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Jaeger, K.M.* ; Nissen, M.* ; Rahm, S.* ; Titzmann, A.* ; Fasching, P.A.* ; Beilner, J.* ; Eskofier, B.M. ; Leutheuser, H.*

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

Physiol. Meas. 45:055009 (2024)
Verlagsversion DOI
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Biomedical Signal Analysis ; Digital Health ; Fetal Electrocardiography ; Fetal Qrs Detection ; Prenatal Care ; Wearable Sensing; Ecg Extraction
ISSN (print) / ISBN 0967-3334
e-ISSN 1361-6579
Zeitschrift Physiological Measurement
Quellenangaben Band: 45, Heft: 5, Seiten: , Artikelnummer: 055009 Supplement: ,
Verlag Institute of Physics Publishing (IOP)
Verlagsort Temple Circus, Temple Way, Bristol Bs1 6be, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Förderungen Bundesministerium fr Gesundheithttp://dx.doi.org/10.13039/501100003107