Information Theory to probe Intrapartum Fetal Heart Rate Dynamics

TL;DR

This study applies information theory, especially auto mutual information, to analyze fetal heart rate dynamics during labor, demonstrating improved detection of fetal acidosis and revealing changes in entropy and structure over labor stages.

Contribution

It introduces the use of auto mutual information in FHR analysis, outperforming traditional entropy measures for early detection of fetal acidosis.

Findings

Auto mutual information outperforms ApEn and SampEn in acidosis detection.

Shannon entropy increases as labor progresses.

Fetuses with acidosis show more structured FHR dynamics.

Abstract

Intrapartum fetal heart rate (FHR) monitoring constitutes a reference tool in clinical practice to assess the baby health status and to detect fetal acidosis. It is usually analyzed by visual inspection grounded on FIGO criteria. Characterization of Intrapartum fetal heart rate temporal dynamics remains a challenging task and continuously receives academic research efforts. Complexity measures, often implemented with tools referred to as \emph{Approximate Entropy} (ApEn) or \emph{Sample Entropy} (SampEn), have regularly been reported as significant features for intrapartum FHR analysis. We explore how Information Theory, and especially {\em auto mutual information} (AMI), is connected to ApEn and SampEn and can be used to probe FHR dynamics. Applied to a large (1404 subjects) and documented database of FHR data, collected in a French academic hospital, it is shown that i) auto mutual information outperforms ApEn and SampEn for acidosis detection in the first stage of labor and continues to yield the best performance in the second stage; ii) Shannon entropy increases as labor progresses, and is always much larger in the second stage;iii) babies suffering from fetal acidosis additionally show more structured temporal dynamics than healthy ones and that this progressive structuration can be used for early acidosis detection.