Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform

Ali Shahidi Zandi, Manouchehr Javidan, Guy A. Dumont, Reza Tafreshi

Research output: Contribution to journalArticle

124 Citations (Scopus)


A novel wavelet-based algorithm for real-time detection of epileptic seizures using scalp EEG is proposed. In a movingwindow analysis, the EEG from each channel is decomposed by wavelet packet transform. Using wavelet coefficients from seizure and nonseizure references, a patient-specific measure is developed to quantify the separation between seizure and nonseizure states for the frequency range of 1-30 Hz. Utilizing this measure, a frequency band representing themaximum separation between the two states is determined and employed to develop a normalized index, called combined seizure index (CSI). CSI is derived for each epoch of every EEG channel based on both rhythmicity and relative energy of that epoch as well as consistency among different channels. Increasing significantly during ictal states, CSI is inspected using one-sided cumulative sum test to generate proper channel alarms. Analyzing alarms from all channels, a seizure alarm is finally generated. The algorithm was tested on scalp EEG recordings from 14 patients, totaling ∼75.8 h with 63 seizures. Results revealed a high sensitivity of 90.5%, a false detection rate of 0.51 h-1 and a median detection delay of 7 s. The algorithm could also lateralize the focus side for patients with temporal lobe epilepsy.

Original languageEnglish
Article number5484951
Pages (from-to)1639-1651
Number of pages13
JournalIEEE Transactions on Biomedical Engineering
Issue number7
Publication statusPublished - Jul 2010



  • EEG
  • epilepsy
  • seizure detection
  • seizure focus lateralization
  • wavelet packet (WP) transform

ASJC Scopus subject areas

  • Biomedical Engineering

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