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Related Experiment Video

Updated: Sep 10, 2025

The Hypoxic Ischemic Encephalopathy Model of Perinatal Ischemia
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Time-Dependent Association Between Cardiotocographic Features and Hypoxic-Ischemic Encephalopathy.

Johann Vargas-Calixto1, Yvonne W Wu2,3, Michael Kuzniewicz4

  • 1Department of Biomedical Engineering, McGill University, Montreal, QC H3A 2B4, Canada.

IEEE Access : Practical Innovations, Open Solutions
|August 25, 2025
PubMed
Summary

Cardiotocography (CTG) analysis for predicting neonatal hypoxic-ischemic encephalopathy (HIE) improves when considering time to delivery. Accounting for this factor strengthens the association between specific CTG features and HIE risk.

Keywords:
Acidosisclassificationfetal heart ratehypoxic-ischemic encephalopathylabor and deliverymachine learningmutual information

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Last Updated: Sep 10, 2025

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Early Pathological and Magnetic Resonance Detection of Cerebral Injury Using a Rat Model of Neonatal Hypoxic Ischemic Encephalopathy

Published on: October 28, 2022

841

Area of Science:

  • Perinatal medicine
  • Biomedical signal processing
  • Neonatal neurology

Background:

  • Neonatal hypoxic-ischemic encephalopathy (HIE) is a severe birth complication.
  • Cardiotocography (CTG) monitors fetal heart rate (FHR) and uterine pressure (UP) to assess risk.
  • Current CTG analysis methods do not account for its nonstationary nature or time-varying properties.

Purpose of the Study:

  • To investigate the association between CTG features and HIE development.
  • To determine if incorporating time to delivery (TTD) enhances these associations.
  • To identify relevant CTG features for improved HIE risk prediction.

Main Methods:

  • Analysis of 88 features from FHR and UP signals in 25,197 deliveries.
  • Categorization of infants into HIE, acidosis, and healthy groups based on blood gas exams.
  • Quantification of associations using normalized mutual information, considering TTD.

Main Results:

  • All analyzed CTG features demonstrated variation with TTD.
  • 40 out of 88 CTG features showed significant associations with HIE development.
  • Accounting for TTD strengthened the association for 26 of these HIE-related features.

Conclusions:

  • Many CTG features lack significant association with labor outcomes.
  • Specific CTG features are strongly associated with HIE risk.
  • Automated HIE prediction models should utilize time-varying CTG features and incorporate TTD for improved accuracy.