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AuthorPromit, Basak
AuthorNazmus Sakib, A.H.M
AuthorChowdhury, Muhammad E.H.
AuthorAl-Emadi, Nasser
AuthorCagatay Yalcin, Huseyin
AuthorPedersen, Shona
AuthorMahmud, Sakib
AuthorKiranyaz, Serkan
AuthorAl-Maadeed, Somaya
Available date2023-09-26T05:12:58Z
Publication Date2024-01-31
Publication NameExpert Systems with Applications
Identifierhttp://dx.doi.org/10.1016/j.eswa.2023.121196
ISSN09574174
URIhttps://www.sciencedirect.com/science/article/pii/S0957417423016986
URIhttp://hdl.handle.net/10576/47960
AbstractThe non-invasive fetal electrocardiogram (fECG) enables easy detection of developing heart abnormalities, leading to a significant reduction in infant mortality rate and post-natal complications. Due to the overlapping of maternal and fetal R-peaks, the low amplitude of the fECG, systematic and ambient noises, typical signal extraction methods, such as adaptive filters, independent component analysis, empirical mode decomposition, etc., are unable to produce satisfactory fECG. While some techniques can produce accurate QRS waves, they often ignore other important aspects of the ECG. Utilizing extensive preprocessing and an appropriate framework, our approach, built upon 1D CycleGAN, achieves fECG signal reconstruction from the mECG signal while preserving its morphology. The performance of our solution was evaluated by combining two available datasets from Physionet, “Abdominal and Direct Fetal ECG Database” and “Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations”, where it achieved an average PCC and Spectral-Correlation score of 88.4% and 89.4%, respectively. It detects the fQRS of the signal with accuracy, precision, recall and F1 score of 92.6%, 97.6%, 94.8% and 96.4%, respectively. It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0.25% and 0.27%, respectively. The main contribution of our work is that, unlike similar studies, it can retain the morphology of the ECG signal with high fidelity. The accuracy of our solution for fetal heart rate and R-R interval length is comparable to existing state-of-the-art techniques. This makes it a highly effective tool for early diagnosis of fetal heart diseases and regular health checkups of the fetus.
SponsorThis study was funded by Qatar University Grant: QUHI-CENG-22/23-548.
Languageen
PublisherElsevier
SubjectFetal ECG
ECG Extraction
Deep Learning
Heart Rate Estimation
Heart Rate Variability
CycleGAN
TitleA novel deep learning technique for morphology preserved fetal ECG extraction from mother ECG using 1D-CycleGAN
TypeArticle
Volume Number235
dc.accessType Full Text


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