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

Authors:
Bertha Guijarro-Berdiñas;Amparo Alonso-Betanzos;Oscar Fontenla-Romero;Olga Garcia-Dans;Noelia Sánchez-Maroño
Affiliations:
-;-;-;-;-
Venue:
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Year:
2001

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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.