Seizure detection in temporal lobe epileptic EEGs using the best basis wavelet functions

Berdakh Abibullaev, Min Soo Kim, Hee Don Seo

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


In this paper, we propose a novel method using best basis wavelet functions and double thresholding that are well suited for detecting and localization of important epileptic events from noisy recorded seizure EEG signals. Our technique is based on dyadic wavelet decomposition and is mainly concerned detection of single epileptic transients within the observation sequence, such as ictal and interictal epochs of EEG. In our experiment we use temporal lobe epileptic data recorded during 84 h from four patients diagnosed with epilepsy. We have achieved promising results that demonstrate efficiency and simplicity that can be used in clinical studies as an automatic decision support tool. Thus reduce the physician's workload and provide accurate diagnosis of epileptic seizures.

Original languageEnglish
Pages (from-to)755-765
Number of pages11
JournalJournal of Medical Systems
Issue number4
Publication statusPublished - Aug 2010


  • EEG
  • Epileptic seizure detection
  • Neural networks
  • Temporal lobe epilepsy
  • Wavelet transforms

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management


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