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Radial Basis Function Neural Network for Classification of Quantitative EEG in Patients with Advanced Chronic Renal Failure

  • Juan A. Barios
  • César Gonzalez
  • Bettina Benbunan
  • Victor Fernández-Armayor
  • José L. Teruel
  • Milagros Fernández
  • Antonio Pedrera
  • José M. Gaztelu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

In this study we investigate the potential of applying the radial basis function (RBF) neural network architecture for the classification of patients with chronic renal failure (CRF) through quantitative parameters derived from EEG. To provide an objective EEG assessment of cerebral disturbances in CRF, we set up and tested a procedure of classification based on artificial neural networks (ANN) using RBF trained with quantitative parameters derived from EEG. A set sample was prepared based on EEG of 17 patients and 18 age-matched control subjects. Quantitative EEG (qEEG) found significant differences between groups. Accuracy of ANN-based classification in this set was 86.6%. Our results indicate that a classification system based on RBF neural networks may help in the automation of EEG analysis for diagnosis and prospective clinical evaluation of CRF patients.

Keywords

chronic renal failure hemodialysis qEEG signal analysis artificial neural network classification radial basis function 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Juan A. Barios
    • 1
  • César Gonzalez
    • 2
  • Bettina Benbunan
    • 3
  • Victor Fernández-Armayor
    • 3
  • José L. Teruel
    • 1
  • Milagros Fernández
    • 1
  • Antonio Pedrera
    • 1
  • José M. Gaztelu
    • 1
  1. 1.Instituto de Investigación Ramón y Cajal (IRYCIS)Hospital Universitario Ramón y CajalMadridSpain
  2. 2.Deimos Space SL, CantoblancoMadridSpain
  3. 3.Unidad de Ciencias NeurológicasMadridSpain

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