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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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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
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
Journal
Physiological Measurement
Quellenangaben
Volume: 45,
Issue: 5,
Article Number: 055009
Publisher
Institute of Physics Publishing (IOP)
Publishing Place
Temple Circus, Temple Way, Bristol Bs1 6be, England
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of AI for Health (AIH)
Grants
Bundesministerium fr Gesundheithttp://dx.doi.org/10.13039/501100003107