PDF(Pubmed)
Abstract：
Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions. Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks. Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework. Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.
摘要：
简介：主动监测健康技术的可用性对于降低胎儿死亡率和避免胎儿健康并发症至关重要。在大流行等恶劣情况下，地震,和低资源设置，全球许多医疗保健系统在提供基本服务方面无能，尤其是孕妇,是至关重要的。在这种情况下，能够以直接和快速的方式在医院和家庭中连续监测胎儿非常重要。方法：通过使用清晰的胎儿心电图（ECG）信号计算重要的生物信号措施，可以潜在地实现对婴儿健康的监测。这项研究的目的是开发一个框架，以直接从12通道腹部复合信号中检测和识别胎儿ECG的R峰。因此，非侵入性地记录了70名孕妇(健康和有健康状况)的信号,没有胎儿异常记录.所提出的模型采用递归神经网络架构来稳健地检测胎儿ECGR峰。结果：为了测试提出的框架，我们进行了受试者依赖性(5倍交叉验证)和独立(离开一个受试者)测试.提出的框架实现了94.2%和88.8%的平均精度值，分别。更具体地说,在wrnixcaseosa层形成的挑战性时期，留一受试者检验的准确性为86.7％。此外，我们根据检测到的R峰计算了胎儿心率，演示结果突出了所提出框架的鲁棒性。讨论：这项工作有可能迎合孕产妇和胎儿保健的关键行业以及推进相关应用。
