Abstract Antepartum Foetal surveillance is the most vital epoch of investigation during the pregnancy period. This surveillance would provide an opening to plan and manage the Foetus during Intrapartum and Antepartum stages of pregnancy. Moreover, it will help to identify high risk Foetuses during pregnancies which are complicated by maternal health conditions like diabetes mellitus, intrauterine growth restriction, etc. The foetal electrocardiogram (fECG) signal can be detected in the course of pregnancy from the Antepartum stage. Generally, fECG signal analysis is not carried out for Foetal surveillance. Rather, the traditional methodologies like phonocardiogram, etc. are being utilized. The reason is the unavailability of an effective methodology for providing good quality fECG signal. The proposal of a hybrid tactic called Bayesian Deep Belief Network (BDBN) for fECG signal enhancement is presented in this article. The proposed BDBN technique involves Baye’s filtering methodology in amalgamation with Deep Belief Network. The Baye’s filtering was employed to eliminate undesired signal components. Deep learning (DL) technique was utilized with Deep belief network (DBN) to extract high quality fECG signal. The methodology resulted with good quality fECG signal which is indeed valuable for timely Physician analysis.

中文翻译：

---

增强的产前胎儿心电图信号的复合深度信念网络方法

摘要 产前胎儿监测是孕期最重要的调查时期。这种监测将为计划和管理怀孕期间和产前阶段的胎儿提供一个机会。此外，它还有助于识别怀孕期间因糖尿病、宫内生长受限等母亲健康状况而复杂化的高危胎儿。 胎儿心电图 (fECG) 信号可以在怀孕过程中从 Antepartum 阶段检测到。通常，胎儿监测不进行 fECG 信号分析。相反，正在使用传统的方法，如心音图等。原因是没有提供高质量 fECG 信号的有效方法。本文提出了一种称为贝叶斯深度置信网络 (BDBN) 的混合策略，用于 fECG 信号增强。提议的 BDBN 技术涉及 Baye 过滤方法与 Deep Belief Network 的融合。贝叶斯滤波被用来消除不需要的信号分量。深度学习 (DL) 技术与深度置信网络 (DBN) 一起使用来提取高质量的 fECG 信号。该方法产生了高质量的 fECG 信号，这对于及时的医生分析确实很有价值。深度学习 (DL) 技术与深度置信网络 (DBN) 一起使用来提取高质量的 fECG 信号。该方法产生了高质量的 fECG 信号，这对于及时的医生分析确实很有价值。深度学习 (DL) 技术与深度置信网络 (DBN) 一起使用来提取高质量的 fECG 信号。该方法产生了高质量的 fECG 信号，这对于及时的医生分析确实很有价值。