On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines

Ejay Nsugbe, Jose Javier Reyes‐Lagos, Dawn Adams, Oluwarotimi Williams Samuel

Research output: Contribution to journalLetterpeer-review

2 Citations (Scopus)
34 Downloads (Pure)

Abstract

Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health‐based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of south American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre‐processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated.
Original languageEnglish
Pages (from-to)11-22
Number of pages12
JournalHealth Technology Letters
Volume10
Issue number1-2
Early online date8 Apr 2023
DOIs
Publication statusPublished (in print/issue) - 30 Apr 2023

Bibliographical note

Funding Information:
The authors would like to thank Brian Kerr from Kerr Editing for proofreading this manuscript.

Publisher Copyright:
© 2023 The Authors. Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Keywords

  • support vector machines
  • physiological models
  • signal classification
  • medical signal processing
  • learning (artificial intelligence)
  • decision support systems
  • biocybernetics
  • medical control systems

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