A New Approach for Diagnosing Epilepsy by Using Wavelet Transform and Neural Networks

Abstract

Today, epilepsy keeps its importance as a major brain disorder. However, although some devices such as magnetic resonance (MR), brain tomography (BT) are used to diagnose the structural disorders of brain, for observing some special illnesses especially such as epilepsy, EEG is routinely used for observing the epileptic seizures, in neurology clinics. In our study, we aimed to classify the EEG signals and diagnose the epileptic seizures directly by using wavelet transform and an artificial neural network model. EEG signals are separated into delta, theta, alpha, and beta spectral components by using wavelet transform. These spectral components are applied to the inputs of the neural network. Then, neural network is trained to give three outputs to signify the health situation of the patients

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA410462

Entities

People

  • I. Turkoglu
  • M. A. Arserim
  • M. Akin
  • M. K. Kiymik

Organizations

  • Dicle University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acquisition
  • Computers
  • Data Acquisition
  • Data Sets
  • Diseases And Disorders
  • Engineering
  • Epilepsy
  • Frequency
  • Learning
  • Machine Learning
  • Magnetic Resonance
  • Neural Networks
  • Resonant Frequency
  • Stationary
  • Training
  • Wavelet Transforms
  • Waves

Readers

  • Educational Psychology
  • Neuroscience
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks