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Discriminating Power of an Sample Entropy and a Nonlinear Association Index in Prediction of a Preterm Labor Based on Electrohysterographical Signals

  • Dariusz Radomski
  • Agnieszka Małkiewicz
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

Recognition of physiological and pathological patterns in biomedical signals is still a challenge. The are many linear and nonlinear techniques proposed for this purpose but their effectiveness is seldom compared and ranked. The aim of the paper was to compare a discriminating power of an sample entropy and a nonlinear association index in prediction of a labor based on electrohysterographical (EHG) signals. The EHG signals were registered at women being during a labor or waiting for beginning of a labor. A sample entropy was estimated for a single component of an EHG signal. A nonlinear association index was computed to express a plausible relation between two components of an EHG signal. The comparison of usefullness of these parameters in a labor prediction was performed using ROC. The obtained results show that a labor prediction based on the nonlinear association index is more effective than using the sample entropy.

Keywords

Uterine Contraction Sample Entropy Bioelectrical Activity Uterine Activity Pathological Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dariusz Radomski
    • 1
  • Agnieszka Małkiewicz
    • 1
  1. 1.Institure of RadioelectronicsWarsaw University of TechnologyWarsawPoland

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