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Related Concept Videos

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Significance Testing: Overview01:04

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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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Friedman Two-way Analysis of Variance by Ranks01:21

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

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Does maternal respiration modulate maternal-fetal cardiovascular coupling?

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Related Experiment Video

Updated: Jul 13, 2026

Comprehensive Analysis of Transcription Dynamics from Brain Samples Following Behavioral Experience
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New possibilities for ST analysis - A post-hoc analysis on the Dutch STAN RCT.

A D J Hulsenboom1, M B Van der Hout-van der Jagt2, E S A van den Akker3

  • 1Department of Obstetrics and Gynecology, Máxima Medical Center, P.O. Box 7777, 5500 MB Veldhoven, the Netherlands.

Early Human Development
|January 29, 2022
PubMed
Summary

Adjusting fetal electrocardiogram (fECG) ST event analysis with relative baseline changes improves detection of fetal metabolic acidosis. This refined method offers higher sensitivity and specificity, enhancing diagnostic accuracy during labor.

Keywords:
Fetal electrocardiographyFetal monitoringMetabolic acidosisST eventT/QRS

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Area of Science:

  • Obstetrics and Gynecology
  • Fetal Monitoring
  • Cardiology

Background:

  • The diagnostic accuracy of ST analysis of the fetal electrocardiogram (fECG) during labor is currently uncertain.
  • Physiological variations in fetal heart axis can lead to false alarms (ST events).
  • Adjusting ST events based on relative changes from baseline may improve diagnostic accuracy.

Purpose of the Study:

  • To determine the optimal cut-off for relative ST events in fECG to accurately detect fetal metabolic acidosis.

Main Methods:

  • Post-hoc analysis of fECG tracings from the Dutch STAN trial (STAN+CTG branch).
  • Inclusion of 1328 term singleton fetuses with fetal scalp ECG during labor.
  • Utilized receiver operating characteristic (ROC) curve analysis to identify the optimal cut-off value.

Main Results:

  • The optimal cut-off for relative baseline ST events was determined to be an 85% increment from baseline.
  • Relative ST events demonstrated a sensitivity of 90% and a specificity of 80% for detecting metabolic acidosis.

Conclusions:

  • Adjusting the definition of ST events using relative changes can enhance ST analysis.
  • This improved ST analysis may become independent of cardiotocography (CTG) interpretation, leading to more reliable diagnostics.