Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods
This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead. It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimensio...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020035 |
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| author | Said Ziani Yousef Farhaoui Mohammed Moutaib |
| author_facet | Said Ziani Yousef Farhaoui Mohammed Moutaib |
| author_sort | Said Ziani |
| collection | DOAJ |
| description | This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead. It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). Due to the highly disproportionate frequency of the fetus’s heart rate compared to the mother’s, the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy. Furthermore, we can disentangle the various components of fetal ECG, which serve as inputs to the CNN model to optimize the actual FECG signal, denoted by FECGr, which is recovered using the SVD-ICA process. The findings demonstrate the efficiency of this innovative approach, which may be deployed in real-time. |
| format | Article |
| id | doaj-art-4d9db15e4968489f808cf92c27a8ccc4 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-4d9db15e4968489f808cf92c27a8ccc42025-02-03T09:17:07ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-09-016330131010.26599/BDMA.2022.9020035Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF MethodsSaid Ziani0Yousef Farhaoui1Mohammed Moutaib2Research Group in Biomedical Engineering and Pharmaceutical Sciences, ENSAM, Mohammed V University, Rabat 10090, Morocco, and the High School of Technology ESTC, University of Hassan II, Casablanca 20153, Morocco.STI Laboratory, T-IDMS, Faculty of Sciences and Techniques, Moulay Ismail University of Meknes, Errachidia 52000, Morocco.IMAGE Laboratory, University of Moulay Ismail, Meknes 50000, Morocco.This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead. It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). Due to the highly disproportionate frequency of the fetus’s heart rate compared to the mother’s, the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy. Furthermore, we can disentangle the various components of fetal ECG, which serve as inputs to the CNN model to optimize the actual FECG signal, denoted by FECGr, which is recovered using the SVD-ICA process. The findings demonstrate the efficiency of this innovative approach, which may be deployed in real-time.https://www.sciopen.com/article/10.26599/BDMA.2022.9020035fetal electrocardiogramconvolutional neural network (cnn)deep learning (dl)feature extraction |
| spellingShingle | Said Ziani Yousef Farhaoui Mohammed Moutaib Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods Big Data Mining and Analytics fetal electrocardiogram convolutional neural network (cnn) deep learning (dl) feature extraction |
| title | Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods |
| title_full | Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods |
| title_fullStr | Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods |
| title_full_unstemmed | Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods |
| title_short | Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods |
| title_sort | extraction of fetal electrocardiogram by combining deep learning and svd ica nmf methods |
| topic | fetal electrocardiogram convolutional neural network (cnn) deep learning (dl) feature extraction |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020035 |
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