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Big Data Mining and Analytics 2023, 6(3): 301-310 https://doi.org/10.26599/BDMA.2022.9020035
Open Access | Issue | Published: 07 April 2023

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Show Author's Information Said Ziani1( ),  Yousef Farhaoui2,  Mohammed Moutaib3
Research 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.
Keywords:
Convolutional Neural Network (CNN), feature extraction, Deep Learning (DL), fetal electrocardiogram
Cite this article:
Ziani S, Farhaoui Y, Moutaib M. Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods. Big Data Mining and Analytics, 2023, 6(3): 301-310. https://doi.org/10.26599/BDMA.2022.9020035
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Abstract Full text About this article

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.


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Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Show Author's information Said Ziani1( ), Yousef Farhaoui2, Mohammed Moutaib3
Research 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.

Abstract

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.

Keywords: Convolutional Neural Network (CNN), feature extraction, Deep Learning (DL), fetal electrocardiogram

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Received: 28 August 2022
Revised: 19 September 2022
Accepted: 27 September 2022
Published: 07 April 2023
Issue date: September 2023

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