In the last two decades, stillbirth has caused around 2 million fetal deaths worldwide. Although current ultrasound tools are reliably used for the assessment of fetal growth during pregnancy, it still raises safety issues on the fetus, requires skilled providers, and has economic concerns in less developed countries. Here, we propose deep coherence, a novel artificial intelligence (AI) approach that relies on 1 min non-invasive electrocardiography (ECG) to explain the association between maternal and fetal heartbeats during pregnancy. We validated the performance of this approach using a trained deep learning tool on a total of 941 one minute maternal-fetal R-peaks segments collected from 172 pregnant women (20–40 weeks). The high accuracy achieved by the tool (90%) in identifying coupling scenarios demonstrated the potential of using AI as a monitoring tool for frequent evaluation of fetal development. The interpretability of deep learning was significant in explaining synchronization mechanisms between the maternal and fetal heartbeats. This study could potentially pave the way toward the integration of automated deep learning tools in clinical practice to provide timely and continuous fetal monitoring while reducing triage, side-effects, and costs associated with current clinical devices.
The accurate estimation of the Gestational Age (GA) in fetal development studies has the potential to detect health issues at early stages of pregnancy. In this article, we adopt the Support Vector Machine (SVM) tool to investigate whether gold standard GA can be reliably estimated by using maternal as well as fetal Heart Rate Variability (HRV) features. The study considered Electrocardiogram (ECG) signals from 60 pregnant women. Maternal and fetal HRV parameters were calculated, and SVM regression with the linear kernel function was utilized to produce a robust estimate of fetal age. By evaluating the crossvalidation performances, we found that maternal electrophysiological parameters contribute to the correct estimation of the GA. Results showed that the linear kernel maintains better performance over the radial basis function kernel in the SVM-based regression models. Compared with gold standard GA identified by CRL, the proposed model resulted in an error of 5.11 weeks, Bland-Altman estimated bias of -0.31 weeks and limits of agreement of 8.97 and -9.59 weeks, and Pearson correlation coefficient of 0.63. It can be speculated that the fetal GA can be more reliably estimated when incorporating maternal along with fetal HRV parameters using 1 min of ECG signals.
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