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.

对于医生而言，最困难的任务之一是获取高质量的胎儿心电图（fECG），以根据子宫内胎儿的状况进行分析，管理和计划。因此，胎儿心电图信号不适合执行分析以监测胎儿状况。其他传统方法也被用来获取胎儿状况。可以通过使用侵入性或非侵入性技术来获取胎儿心电图信号。由于侵入性技术对胎儿有害，因此大多采用非侵入性技术。胎儿心电图信号只能在胎儿在子宫中发育后的25周后获取，这被称为产前期。本文介绍了使用人工智能技术将深度学习技术用于无创胎儿心电图信号合成的用途。卷积神经网络（CNN），深度置信神经网络（BNN）和反向传播神经网络（BPNN）已针对该提案进行了测试。参考合成的高质量胎儿心电图信号比较结果和表现。