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Application of Neural Networks to Automated Brain Maturation Study

  • L. Moreno
  • J. D. Piñeiro
  • J. L. Sánchez
  • S. Mañas
  • J. Merino
  • L. Acosta
  • A. Hamilton

Abstract

An application of Neural Networks (NN) to brain maturation prediction is presented. The problem consists of, given a pattern extracted from electroencephalographic (EEG) signals, state the degree of brain development (low or normal/high). To that end, a population of subjects with their EEG assessed by a neurologist is available. A Backpropagation (BP) neural network is used for this supervised classification task, and a comparison with standard statistical classifiers is made. The effect on performance of several preprocessing techniques such as Principal Components Analysis (PCA), normalization and scaling is investigated. It is found better performance in the NN approach, both in terms of efficiency and consistency.

Keywords

Linear Discriminant Analysis Training Group Direct Memory Access Quadratic Discriminant Analysis Brain Maturation 
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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References

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Copyright information

© Springer-Verlag/Wien 1993

Authors and Affiliations

  • L. Moreno
    • 1
  • J. D. Piñeiro
    • 1
  • J. L. Sánchez
    • 1
  • S. Mañas
    • 2
  • J. Merino
    • 1
  • L. Acosta
    • 1
  • A. Hamilton
    • 1
  1. 1.Dept. of Applied PhysicsUniversity of La LagunaTenerife, Canary IslandsSpain
  2. 2.Dept. of Neurophysiology“Nuestra Señora de la Candelaria” HospitalLa Laguna, TenerifeSpain

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