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An Auto-learning System for the Classification of Fetal Heart Rate Decelerative Patterns

  • Bertha Guijarro-Berdiñas
  • Amparo Alonso-Betanzos
  • Oscar- Fontenla-Romero
  • Olga Garcia-Dans
  • Noelia Sánchez-Maroño
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

The classification of decelerations of the Fetal Heart Rate signal is a difficult and crucial task in order to diagnose the fetal state. For this reason the development of an automatic classifier would be desirable. However, the low incidence of these patterns makes it difficult. In this work, we present a solution to this problem: an auto-learning system, that combines self-organizing artificial neural networks and a rule-based approach, able to incorporate automatically to its knowledge each new pattern detected during its clinical daily use.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Bertha Guijarro-Berdiñas
    • 1
  • Amparo Alonso-Betanzos
    • 1
  • Oscar- Fontenla-Romero
    • 1
  • Olga Garcia-Dans
    • 1
  • Noelia Sánchez-Maroño
    • 1
  1. 1.Laboratory for Research and Development in Artificial Intelligence (LIDIA) Department of Computer ScienceUniversity of A CoruñaA CoruñaSpain

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