Epileptic spike detection using continuous wavelet transforms and artificial neural networks

Berdakh Abibullaev, Hee Don Seo, Min Soo Kim

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


We propose a new method for detection and classification of noisy recorded epileptic transients in Electroencephalograms (EEG) using the continuous wavelet transform (CWT) and artificial neural networks (ANN). The proposed method consists of a segmentation, feature extraction and classification stage. For the feature extraction stage, we use best basis mother wavelet functions and wavelet thresholding technique. For the classification stage, multilayer perceptron neural networks were implemented according to standard backpropagation learning formulations. We demonstrate the efficiency of our feature extraction method on data to improve the ANN detection performance. As a result, we achieved the accuracy in detection and classification of seizure EEG signals with 94.69%, which is relatively good comparing with the available algorithms at present time.

Original languageEnglish
Pages (from-to)33-48
Number of pages16
JournalInternational Journal of Wavelets, Multiresolution and Information Processing
Issue number1
Publication statusPublished - Jan 2010


  • Artificial neural networks
  • Continuous wavelet transforms
  • Epileptic seizure detection

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Applied Mathematics

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