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المؤلفArafat, Rahman
المؤلفMahmud, Sakib
المؤلفChowdhury, Muhammad E.H.
المؤلفYalcin, Huseyin Cagatay
المؤلفKhandakar, Amith
المؤلفMutlu, Onur
المؤلفMahbub, Zaid Bin
المؤلفKamal, Reema Yousef
المؤلفPedersen, Shona
تاريخ الإتاحة2023-05-25T05:00:49Z
تاريخ النشر2023-08-31
اسم المنشورEngineering Applications of Artificial Intelligence
المعرّفhttp://dx.doi.org/10.1016/j.engappai.2023.106414
الرقم المعياري الدولي للكتاب09521976
معرّف المصادر الموحدhttps://www.sciencedirect.com/science/article/pii/S0952197623005985
معرّف المصادر الموحدhttp://hdl.handle.net/10576/43445
الملخصFetal 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.
راعي المشروعThis study was funded by Qatar National Research Fund (QNRF), National Priorities Research Program (NPRP 10-0123-170222).
اللغةen
الناشرElsevier
الموضوعFetal ECG (fECG)
Mother ECG (mECG)
Coronary Heart Disease (CHD)
Signal reconstruction
Deep supervision
Deep Learning
Convolutional Neural Network (CNN)
العنوانFetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model
النوعArticle
رقم المجلد123
dc.accessType Full Text


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