Electrical and Mechanical Modeling of Uterine Contractions Analysis Using Connectivity Methods and Graph Theory

TL;DR

This study develops a framework using simulated EHG signals and graph theory to identify features indicative of uterine connectivity and synchronization, aiding in preterm labor prediction.

Contribution

It introduces a novel simulation-based approach combining electrical and mechanical models to analyze uterine contractions through connectivity features.

Findings

H2 and combined features effectively detect mechanotransduction shifts

Eff, PR, and BC are best for electrical diffusion shifts

Simplified electromechanical models can monitor uterine synchronization

Abstract

Premature delivery is a leading cause of fetal death and morbidity, making the prediction and treatment of preterm contractions critical. The electrohysterographic (EHG) signal measures the electrical activity controlling uterine contraction. Analyzing EHG features can provide valuable insights for labor detection. In this paper, we propose a framework using simulated EHG signals to identify features sensitive to uterine connectivity. We focus on EHG signal propagation during delivery, recorded by multiple electrodes. Simulated EHG signals were generated using electrical diffusion (ED) and mechanotransduction (EDM) to identify which connectivity methods and graph parameters best represent uterine synchronization. The signals were simulated in two scenarios: using only ED by modifying tissue resistance, and using both ED and EDM by varying mechanotransduction model parameters. A matrix of 16 surface electrodes was used for the simulations. Our results show that a simplified electromechanical model can monitor uterine synchronization. Feature selection using Fscore on real and simulated EHG signals highlighted that the best features for detecting mechanotransduction shifts were H2 alone or combined with Str, R2(PR), and ICOH(Str). The best features for detecting electrical diffusion shifts were H2, Eff, PR, and BC.