A Genetic Approach to Selecting the Optimal Feature for Epileptic Seizure Prediction

Abstract

The objective of this study is to (1) develop and apply efficient algorithms to simultaneous intracranial electroencephalographic signals recorded from multiple implanted electrode sites to evaluate the spatial and temporal behavior of seizure precursors and (2) to demonstrate the utility of multiple feature and channel synergy for predicting epileptic seizures in patients with mesial temporal lobe epilepsy. Short-term seizure precursors within a 10-minute time period are investigated. The method consists of preprocessing, processing, feature selection, classification, and validation steps. The preprocessing step removes extraneous data and captures the salient signal attributes while maintaining the integrity of the signal. Processing is a three-step approach that includes first-level features extracted from the raw data, second-level features extracted from first level features, and third-level features extracted from second-level features. A genetic algorithm selects the optimal features off-line from a preselected group of features to serve as the input to the classifier.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410415

Entities

People

  • A. Hinson
  • G. Vachtsevanos
  • J. Echauz
  • M . D'alessandro
  • R. Esteller

Organizations

  • Georgia Tech

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Chromosomes
  • Classification
  • Databases
  • Distribution Functions
  • Epilepsy
  • Feature Selection
  • Frequency
  • Frequency Bands
  • Frequency Response
  • Genetic Algorithms
  • Health Services
  • Literature Surveys
  • Machine Learning
  • Neural Networks
  • Probability
  • Test Sets

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computer Vision.

Technology Areas

  • AI & ML
  • Biotechnology