Detecting and classifying sharp transients in EEG (Electroencephalograph) recording by visual screening is a laborious and time-consuming task. That is why, there is an urgent need to construct a computer algorithm to detect automatically that type of EEG transient phenomena. The use of an artificial neural network as a classifier and wavelet analysis as pre-processing give promising results to answer that need. This paper proposes to develop a new method for the automatic detection of epileptic spikes based on Wavelet Neural Networks (WNN). A proper selection of scaling in WNN is introduced to overcome the problem of very long time duration during training. The result shows that proper selection of wavelet scaling can decrease training duration without decreasing WNN performance.

Original languageEnglish
Title of host publicationProceedings - APCCAS 2002
Subtitle of host publicationAsia-Pacific Conference on Circuits and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)0780376900
Publication statusPublished - 2002
EventAsia-Pacific Conference on Circuits and Systems, APCCAS 2002 - Denpasar, Bali, Indonesia
Duration: 28 Oct 200231 Oct 2002

Publication series

NameIEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS


ConferenceAsia-Pacific Conference on Circuits and Systems, APCCAS 2002
CityDenpasar, Bali


  • Artificial neural networks
  • Biological neural networks
  • Electroencephalography
  • Epilepsy
  • Fault location
  • Neural networks
  • Signal analysis
  • Signal resolution
  • Wavelet analysis
  • Wavelet transforms


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