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A Fetal Well-Being Diagnostic Method Based on Cardiotocographic Morphological Pattern Utilizing Autoencoder and

Haad Akmal1, Fırat Hardalaç1, Kubilay Ayturan1

  • 1Department of Electrical and Electronics Engineering, Gazi University, Ankara 06570, Turkey.

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Summary

A new machine learning model accurately diagnoses fetal conditions using cardiotocography (CTG) data. This tool aids in managing high-risk pregnancies by classifying fetal status and CTG patterns with high precision.

Keywords:
Bayesian optimizationcardiotocographyclassificationdiagnosticsfeature extractionfeature selectionfetal heart ratefetal well-beingmachine learning

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Area of Science:

  • Perinatal medicine
  • Artificial intelligence in healthcare
  • Signal processing for biomedical applications

Background:

  • Cardiotocography (CTG) is crucial for monitoring fetal well-being by measuring fetal heart rate (FHR) and uterine contractions (UC).
  • Diagnosing fetal distress from CTG requires analyzing complex patterns and addressing data imbalances.
  • Existing methods may benefit from advanced computational approaches for improved accuracy and decision support.

Purpose of the Study:

  • To develop and evaluate a machine learning model for diagnosing fetal conditions and classifying CTG patterns.
  • To address the challenge of imbalanced datasets common in CTG analysis.
  • To create a potential decision support tool for managing high-risk pregnancies.

Main Methods:

  • Utilized an autoencoder for feature extraction and recursive feature elimination for feature selection.
  • Employed Bayesian optimization for model tuning.
  • Integrated Random Forest classifier for fetal status and CTG pattern classification.
  • Addressed dataset imbalance issues inherent in CTG data.

Main Results:

  • Achieved 96.62% accuracy in fetal status classification and 94.96% in CTG morphological pattern classification.
  • Demonstrated high predictive accuracy for specific conditions: 98% for Suspect and 98.6% for Pathologic cases.
  • The model effectively handled imbalanced CTG data, showing robust performance.

Conclusions:

  • The proposed machine learning model shows significant potential as a decision support tool in perinatal care.
  • Accurate classification of fetal status and CTG patterns can enhance the management of high-risk pregnancies.
  • This approach offers a promising advancement in leveraging AI for improved obstetric monitoring.