Identification of seizure onset zone using automatically detected spike and high-frequency oscillation in human intracranial EEG

Su Liu, Zhiyi Sha, Aviva Abosch, Thomas Henry, Nuri Firat Ince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

High frequency oscillations (HFOs) have been considered as reliable biomarkers for seizure onset zone (SOZ) that may potentially benefit the presurgical evaluation in epilepsy surgery. By applying an automatic technique, we explored the spatial characteristics of ripples (80-250 Hz), fast ripples (250-500 Hz) and spikes using human iEEG data recorded in 5 patients with refractory temporal epilepsy, and related our results to clinician-identified SOZ. Fast ripples, which generally appeared within the spiking regions, reached 100% of specificity when used for SOZ approximation, whereas spikes showed highest sensitivity of 92%. Our results indicate that the information of HFOs and spikes can be fused together to better understand the pathophysiology of the disease. Automatic detectors can be used efficiently to identify different types of neural activities, and thus facilitate the accurate delineation of SOZ.

Original languageEnglish (US)
Title of host publication2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2241-2244
Number of pages4
ISBN (Electronic)9781509016792
DOIs
StatePublished - Jun 20 2016
Event24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey
Duration: May 16 2016May 19 2016

Publication series

Name2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings

Other

Other24th Signal Processing and Communication Application Conference, SIU 2016
CountryTurkey
CityZonguldak
Period5/16/165/19/16

Keywords

  • Gaussian Mixture Model clustering
  • High-frequency oscillation
  • Seizure onset zone
  • Spike
  • Time-frequency analysis
  • iEEG

Fingerprint Dive into the research topics of 'Identification of seizure onset zone using automatically detected spike and high-frequency oscillation in human intracranial EEG'. Together they form a unique fingerprint.

  • Cite this

    Liu, S., Sha, Z., Abosch, A., Henry, T., & Ince, N. F. (2016). Identification of seizure onset zone using automatically detected spike and high-frequency oscillation in human intracranial EEG. In 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings (pp. 2241-2244). [7496221] (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2016.7496221