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)
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
Typ der Hochschulschrift
Herausgeber
Schlagwörter
Biomedical Signal Analysis ; Digital Health ; Fetal Electrocardiography ; Fetal Qrs Detection ; Prenatal Care ; Wearable Sensing; Ecg Extraction
Keywords plus
Sprache
englisch
Veröffentlichungsjahr
2024
Prepublished im Jahr
0
HGF-Berichtsjahr
2024
ISSN (print) / ISBN
0967-3334
e-ISSN
1361-6579
ISBN
Bandtitel
Konferenztitel
Konferzenzdatum
Konferenzort
Konferenzband
Quellenangaben
Band: 45,
Heft: 5,
Seiten: ,
Artikelnummer: 055009
Supplement: ,
Reihe
Verlag
Institute of Physics Publishing (IOP)
Verlagsort
Temple Circus, Temple Way, Bristol Bs1 6be, England
Tag d. mündl. Prüfung
0000-00-00
Betreuer
Gutachter
Prüfer
Topic
Hochschule
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Fakultät
Veröffentlichungsdatum
0000-00-00
Anmeldedatum
0000-00-00
Anmelder/Inhaber
weitere Inhaber
Anmeldeland
Priorität
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of AI for Health (AIH)
POF Topic(s)
30205 - Bioengineering and Digital Health
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-540008-001
Förderungen
Bundesministerium fr Gesundheithttp://dx.doi.org/10.13039/501100003107
Copyright
Erfassungsdatum
2024-07-09