An auto-learning system for the classification of fetal heart rate decelerative patterns

1Citations
Citations of this article
4Readers
Mendeley users who have this article in their library.
Get full text

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. © Springer-Verlag Berlin Heidelberg 2001.

Cite

CITATION STYLE

APA

Guijarro-Berdiñasl, B., Alonso-Betanzos, A., Fontenla-Romero, O., Garcia-Dans, O., & Sánchez-Maroño, N. (2001). An auto-learning system for the classification of fetal heart rate decelerative patterns. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2085, 393–400. https://doi.org/10.1007/3-540-45723-2_47

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free