Using genetic algorithms for automatic recurrent ANN development: An application to EEG signal classification

Daniel Rivero, Vanessa Aguiar-Pulido, Enrique Fernandez-Blanco, Marcos Gestal

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

ANNs are one of the most successful learning systems. For this reason, many techniques have been published that allow the obtaining of feed-forward networks. However, few works describe techniques for developing recurrent networks. This work uses a genetic algorithm for automatic recurrent ANN development. This system has been applied to solve a well-known problem: classification of EEG signals from epileptic patients. Results show the high performance of this system, and its ability to develop simple networks, with a low number of neurons and connections.

Original languageEnglish (US)
Pages (from-to)182-191
Number of pages10
JournalInternational Journal of Data Mining, Modelling and Management
Volume5
Issue number2
DOIs
StatePublished - Jan 1 2013
Externally publishedYes

Keywords

  • ANNs
  • artificial neural networks
  • EEG signals
  • electroencephalogram
  • epilepsy detection
  • epileptic patients
  • GAs
  • genetic algorithms
  • signal classification

ASJC Scopus subject areas

  • Management Information Systems
  • Modeling and Simulation
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Using genetic algorithms for automatic recurrent ANN development: An application to EEG signal classification'. Together they form a unique fingerprint.

Cite this