Feature selection before EEG classification supports the diagnosis of Alzheimer's disease.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Published Date: Jul 14, 2017

Abstract

OBJECTIVE: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered to find relevant/non-redundant features. This study aimed to evaluate the relevance of FS approaches applied to Alzheimer's Disease (AD) EEG-based diagnosis and compare the selected features with previous clinical findings.

Authors

  • L R Trambaiolli
    Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo André, Brazil. Electronic address: lucasrtb@gmail.com.
  • N Spolaôr
    Laboratório de Bioinformática, Centro de Engenharia e Ciências Exatas, Universidade Estadual do Oeste do Paraná, Foz do Iguaçu, Brazil.
  • A C Lorena
    Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, São José dos Campos, Brazil.
  • R Anghinah
    Reference Center for Cognitive Disorders, Hospital das Clínicas, University of São Paulo, Rua Arruda Alvim 206, São Paulo, Brazil.
  • J R Sato
    Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, Santo André, Brazil.

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