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Deep learning strategies for foetal electrocardiogram signal synthesis
Pattern Recognition Letters ( IF 3.255 ) Pub Date : 2020-06-21 , DOI: 10.1016/j.patrec.2020.06.016
D.J. Jagannath; D. Raveena Judie Dolly; J. Dinesh Peter

One of the most difficult tasks for the physicians is to acquire a quality foetal electrocardiogram (fECG) to analyze, manage and plan according to the condition of the foetus in the womb. Hence the foetal electrocardiogram signal is not preferred to execute the analysis to monitor the Foetal condition. Other traditional methods are being used to access the foetal condition. The foetal electrocardiogram signal can be acquired either by using invasive or non- invasive techniques. Since the invasive technique is harmful for the foetus, non-invasive technique is mostly adopted. The foetal electrocardiogram signal can be acquired only after twenty five weeks the foetus is developed in the womb, which is referred as the Antepartum period. This article portrays the use of Deep learning techniques for non-invasive foetal electrocardiogram signal synthesis using artificial intelligent techniques. Convolutional neural network (CNN), Deep belief neural networks (BNN) and Back propagation Neural Network (BPNN) have been utilized and tested for the proposal. The outcomes and performance are compared with reference to the synthesized high quality foetal electrocardiogram signal.
更新日期:2020-06-28

 

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