Model for classifying EEG signals using deep learning to identify epileptic seizures

Authors

  • Department of Computer Science, College of Education for pure Sciences, University of Thi-Qar, Iraq
  • Department of Computer Science, College of Education for pure Sciences, University of Thi-Qar, Iraq

DOI:

https://doi.org/10.32792/jeps.v13i1.246

Keywords:

Artificial Neural Network (ANN), Discrete Wavelet Transform(DWT), electroencephalogram (EEG)

Abstract

The neurological condition known as epilepsy affects the brain in a way that causes recurrent problems.
As a result, seizure identification is crucial to the clinical treatment of epileptic patients. The
electroencephalogram (EEG) has become a crucial tool most of the time, a small number of very
competent professionals manually identify the epileptic EEG signal. Detecting and evaluating epileptic
seizure activity in humans. In this paper, we attempt to automate the detecting procedure. We extract
features using the wavelet transform and derive statistical parameters from the wavelet coefficients that
have been decomposed. Artificial neural network (ANN) is used for the classification. By utilizing the
University of Bonn's benchmark database.

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Published

2023-04-10

Issue

Vol. 13 No. 1 (2023): Journal of Education for Pure Science- University of Thi-Qar

Section

Articles

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