Deep Convolutional Long Short-Term Memory Network for Fetal Heart Rate Extraction

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

Fetal electrocardiography is a valuable alternative to standard fetal monitoring. Suppression of the maternal electrocardiogram (ECG) in the abdominal measurements, results in fetal ECG signals, from which the fetal heart rate (HR) can be determined. This HR detection typically requires fetal R-peak detection, which is challenging, especially during low signal-to-noise ratio periods, caused for example by uterine activity. In this paper, we propose the combination of a convolutional neural network and a long short-term memory network that directly predicts the fetal HR from multichannel fetal ECG. The network is trained on a dataset, recorded during labor, while the performance of the method is evaluated both on a test dataset and on set-A of the 2013 Physionet /Computing in Cardiology Challenge. The algorithm achieved a positive percent agreement of 92.1% and 98.1% for the two datasets respectively, outperforming a top-performing state-of-the-art signal processing algorithm.
Originele taal-2Engels
Titel42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
SubtitelEnabling Innovative Technologies for Global Healthcare, EMBC 2020
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's608-611
Aantal pagina's4
ISBN van elektronische versie978-1-7281-1990-8
DOI's
StatusGepubliceerd - jul 2020
Evenement42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Montreal, Canada
Duur: 20 jul 202024 jul 2020

Congres

Congres42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
LandCanada
StadMontreal
Periode20/07/2024/07/20

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  • Citeer dit

    Fotiadou, E., Xu, M., van Erp, B., van Sloun, R. J. G., & Vullings, R. (2020). Deep Convolutional Long Short-Term Memory Network for Fetal Heart Rate Extraction. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare, EMBC 2020 (blz. 608-611). [9175442] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EMBC44109.2020.9175442