Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection

Farah Francis, Saturnino Luz, Honghan Wu, Rosemary Townsend, Sarah S Stock

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Fetal hypoxia can cause damaging consequences on babies' such as stillbirth and cerebral palsy. Cardiotocography (CTG) has been used to detect intrapartum fetal hypoxia during labor. It is a non-invasive machine that measures the fetal heart rate and uterine contractions. Visual CTG suffers inconsistencies in interpretations among clinicians that can delay interventions. Machine learning (ML) showed potential in classifying abnormal CTG, allowing automatic interpretation. In the absence of a gold standard, researchers used various surrogate biomarkers to classify CTG, where some were clinically irrelevant. We proposed using Apgar scores as the surrogate benchmark of babies' ability to recover from birth. Apgar scores measure newborns' ability to recover from active uterine contraction, which measures appearance, pulse, grimace, activity and respiration. The higher the Apgar score, the healthier the baby is.We employ signal processing methods to pre-process and extract validated features of 552 raw CTG. We also included CTG-specific characteristics as outlined in the NICE guidelines. We employed ML techniques using 22 features and measured performances between ML classifiers. While we found that ML can distinguish CTG with low Apgar scores, results for the lowest Apgar scores, which are rare in the dataset we used, would benefit from more CTG data for better performance. We need an external dataset to validate our model for generalizability to ensure that it does not overfit a specific population.Clinical Relevance- This study demonstrated the potential of using a clinically relevant benchmark for classifying CTG to allow automatic early detection of hypoxia to reduce decision-making time in maternity units.

Original languageEnglish
Title of host publicationProceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2023
PublisherIEEE
Number of pages4
Volume2023
ISBN (Electronic)979-8-3503-2447-1
ISBN (Print)979-8-3503-2448-8
DOIs
Publication statusPublished - 11 Dec 2023
EventThe 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2023 - Sydney, Australia
Duration: 24 Jul 202327 Jul 2023
Conference number: 45
https://embc.embs.org/2023/

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
ISSN (Print)2375-7477
ISSN (Electronic)2694-0604

Conference

ConferenceThe 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2023
Abbreviated titleEMBC 2023
Country/TerritoryAustralia
CitySydney
Period24/07/2327/07/23
Internet address

Keywords / Materials (for Non-textual outputs)

  • Infant
  • Pregnancy
  • Infant, Newborn
  • Female
  • Humans
  • Cardiotocography/methods
  • Fetal Hypoxia/diagnosis
  • Labor, Obstetric
  • Uterine Contraction
  • Hypoxia/diagnosis
  • Infant, Newborn, Diseases

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