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Intrapartum fetal heart rate (FHR) monitoring constitutes a reference tool inclinical practice to assess the baby health status and to detect fetalacidosis. It is usually analyzed by visual inspection grounded on FIGOcriteria. Characterization of Intrapartum fetal heart rate temporal dynamicsremains 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), haveregularly 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 mutualinformation outperforms ApEn and SampEn for acidosis detection in the firststage 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 inthe second stage;iii) babies suffering from fetal acidosis additionally showmore structured temporal dynamics than healthy ones and that this progressivestructuration can be used for early acidosis detection.

Information Theory to probe Intrapartum Fetal Heart Rate Dynamics