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Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods 被引量:1
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作者 said ziani Yousef Farhaoui Mohammed Moutaib 《Big Data Mining and Analytics》 EI CSCD 2023年第3期301-310,共10页
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 Independe... 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. 展开更多
关键词 Convolutional Neural Network(CNN) feature extraction Deep Learning(DL) fetal electrocardiogram
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