Automatic Diagnosis of Fetal Heart Rate: Comparison of Different Methodological Approaches

Abstract

The cardiotocography (CTG) is the clinical, traditional noninvasive approach to monitor the fetal condition antepartum. CTG analysis is focused on the detection of fetal heart rate parameters, from which the clinicians can identify by eye inspection some patterns associated to fetal activity. However this qualitative method rarely can detect the emergence of fetal pathologies. This study aims at finding new algorithms which can enhance the differences among the normal CTG signals and those presenting anomalies due to a pathological status. On a database of more than 500 recordings, we tested different classification methods to identify normals from potential pathological fetuses. A Multilayer Perception (MLP) neural network and an Adaptive Neuro-Fuzzy Inference System (ANFIS) were compared with classical statistical methods. Both the neural and neuro-fuzzy approaches seem to give better results than any tested statistical classifier.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA412498

Entities

People

  • G. Magenes
  • M. G. Signorini
  • R. Sassi

Organizations

  • Polytechnic University of Milan

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Data Science
  • Data Sets
  • Databases
  • Discriminant Analysis
  • Doppler Effect
  • Factor Analysis
  • Frequency
  • Frequency Domain
  • Heart
  • Heart Rate
  • Information Science
  • Machine Learning
  • Neural Networks
  • Statistical Algorithms
  • Statistical Analysis
  • Test Sets

Readers

  • Neural Network Machine Learning.
  • Toxicology/Environmental Toxicology

Technology Areas

  • AI & ML