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AuthorArafat, Rahman
AuthorMahmud, Sakib
AuthorChowdhury, Muhammad E.H.
AuthorYalcin, Huseyin Cagatay
AuthorKhandakar, Amith
AuthorMutlu, Onur
AuthorMahbub, Zaid Bin
AuthorKamal, Reema Yousef
AuthorPedersen, Shona
Available date2023-05-25T05:00:49Z
Publication Date2023-08-31
Publication NameEngineering Applications of Artificial Intelligence
Identifierhttp://dx.doi.org/10.1016/j.engappai.2023.106414
ISSN09521976
URIhttps://www.sciencedirect.com/science/article/pii/S0952197623005985
URIhttp://hdl.handle.net/10576/43445
AbstractFetal heart monitoring and early disease detection using non-invasive fetal electrocardiograms (fECG) can help substantially to reduce infant death through improved diagnosis of Coronary Heart Disease (CHD) in the fetus. Despite the potential benefits, non-invasive fECG extraction from maternal abdominal ECG (mECG) is a challenging problem due to multiple factors such as the overlap of maternal and fetal R-peaks, low amplitude of fECG, and various systematic and environmental noises. Conventional fECG extraction techniques, such as adaptive filters, independent component analysis (ICA), empirical mode decomposition (EMD), etc., face various performance issues due to the fECG extraction challenges. In this paper, we proposed a novel deep learning architecture, LinkNet++ (motivated by the original LinkNet) to extract fECG from abdominal mECG automatically and efficiently using two different publicly available datasets. LinkNet++ is equipped with a feature-addition method to combine deep and shallow levels with residual blocks to overcome the limitations of U-Net and UNet++ models. It also has deep supervised and densely connected convolution blocks to overcome the limitations of the original LinkNet. The proposed LinkNet++ model was evaluated using fECG signal reconstruction and fetal QRS (fQRS) detection. As a signal-to-signal synthesis model, LinkNet++ performed very well in two real-life datasets and achieved 85.58% and 87.60% Pearson correlation coefficients (PCC) between the ground truth and predicted fECG on two datasets, respectively. In terms of fQRS detection, it also outperformed most of the previous works and showed excellent performance with more than 99% of F1 scores on both datasets. Our results indicate that the proposed model can potentially extract fECG non-invasively with excellent signal quality, thereby providing an excellent diagnostic tool for various fetal heart diseases.
SponsorThis study was funded by Qatar National Research Fund (QNRF), National Priorities Research Program (NPRP 10-0123-170222).
Languageen
PublisherElsevier
SubjectFetal ECG (fECG)
Mother ECG (mECG)
Coronary Heart Disease (CHD)
Signal reconstruction
Deep supervision
Deep Learning
Convolutional Neural Network (CNN)
TitleFetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model
TypeArticle
Volume Number123
dc.accessType Full Text


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