Employing Support Vector Machine Regression to Estimate the Fetal Gestational Age

Abstract

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.