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

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维普

外文摘要：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.

外文关键词：

fetal electrocardiogramConvolutional Neural Network(CNN)Deep Learning(DL)feature extraction

作者：

Said Ziani、Yousef Farhaoui、Mohammed Moutaib

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作者单位：

Research Group in Biomedical Engineering and Pharmaceutical Sciences,ENSAM,Mohammed V University,Rabat 10090,Morocco

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

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出版年：

2023

DOI：

10.26599/BDMA.2022.9020035

大数据挖掘与分析(英文版)

CSCDEI

ISSN：

年,卷(期)：2023.6(3)

参考文献量41