Improved Preterm Prediction Based on Optimized Synthetic Sampling of EHG Signal

TL;DR

This paper introduces an optimized synthetic sampling method for uterine electrohysterogram signals to improve preterm labor prediction accuracy, addressing data scarcity and feature effectiveness issues.

Contribution

It proposes a novel approach to determine the optimal synthetic sample balance, enhancing machine learning prediction of preterm labor from EHG signals.

Findings

Significant improvement in prediction precision on TPEHG database.

Effective balancing of synthetic samples reduces bias and preserves feature importance.

Validated method enhances preterm detection accuracy.

Abstract

Preterm labor is the leading cause of neonatal morbidity and mortality and has attracted research efforts from many scientific areas. The inter-relationship between uterine contraction and the underlying electrical activities makes uterine electrohysterogram (EHG) a promising direction for preterm detection and prediction. Due the scarcity of EHG signals, especially those of preterm patients, synthetic algorithms are applied to create artificial samples of preterm type in order to remove prediction bias towards term, at the expense of a reduction of the feature effectiveness in machine-learning based automatic preterm detecting. To address such problem, we quantify the effect of synthetic samples (balance coefficient) on features' effectiveness, and form a general performance metric by utilizing multiple feature scores with relevant weights that describe their contributions to class separation. Combined with the activation/inactivation functions that characterizes the effect of the abundance of training samples in term and preterm prediction precision, we obtain an optimal sample balance coefficient that compromise the effect of synthetic samples in removing bias towards the majority and the side-effect of reducing features' importance. Substantial improvement in prediction precision has been achieved through a set of numerical tests on public available TPEHG database, and it verifies the effectiveness of the proposed method.