A new method for epileptic waveform recognition using wavelet decomposition and artificial neural networks

L. Szilágyi, Z. Benyó, S. M. Szilágyi

Research output: Contribution to journalConference article

7 Citations (Scopus)

Abstract

The recognition of epileptic waveforms from the electroencephalogram is an important physiological signal processing task, as epilepsy is still one of the most frequent brain disorders. The main goal of this paper is to present a new method to diagnose the epileptic waveforms directly from EEG, by performing a quick signal processing, which makes it possible to apply in on-line monitoring systems. The EEG signal processing is performed in two steps. In the first step, by using the multi-resolution wavelet decomposition, we obtain different spectral components (α, β, δ θ) of the measured signal. These components serve as input signals for the artificial neural network (ANN), which accomplishes the recognition of epileptic waves. The recognition rate for all test signals turned out to be over 95%.

Original languageEnglish
Pages (from-to)2025-2026
Number of pages2
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
Publication statusPublished - Dec 1 2002
EventProceedings of the 2002 IEEE Engineering in Medicine and Biology 24th Annual Conference and the 2002 Fall Meeting of the Biomedical Engineering Society (BMES / EMBS) - Houston, TX, United States
Duration: Oct 23 2002Oct 26 2002

Keywords

  • Artificial neural network
  • EEG
  • Epilepsy
  • Signal processing
  • Wavelet transform

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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