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

doi:10.1109/SIU.2016.7496221

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

Neurology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 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 language	English (US)
Title of host publication	2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2241-2244
Number of pages	4
ISBN (Electronic)	9781509016792
DOIs	https://doi.org/10.1109/SIU.2016.7496221
State	Published - Jun 20 2016
Event	24th Signal Processing and Communication Application Conference, SIU 2016 - Zonguldak, Turkey Duration: May 16 2016 → May 19 2016

Publication series

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

Other

Other	24th Signal Processing and Communication Application Conference, SIU 2016
Country/Territory	Turkey
City	Zonguldak
Period	5/16/16 → 5/19/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

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

Publisher link

10.1109/SIU.2016.7496221

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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). Article 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

Identification of seizure onset zone using automatically detected spike and high-frequency oscillation in human intracranial EEG. / Liu, Su; Sha, Zhiyi; Abosch, Aviva et al.
2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2241-2244 7496221 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Liu, S, Sha, Z, Abosch, A, Henry, T & Ince, NF 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., 7496221, 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 2241-2244, 24th Signal Processing and Communication Application Conference, SIU 2016, Zonguldak, Turkey, 5/16/16. https://doi.org/10.1109/SIU.2016.7496221

Liu S, Sha Z, Abosch A, Henry T, Ince NF. 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. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2241-2244. 7496221. (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings). doi: 10.1109/SIU.2016.7496221

Liu, Su ; Sha, Zhiyi ; Abosch, Aviva et al. / Identification of seizure onset zone using automatically detected spike and high-frequency oscillation in human intracranial EEG. 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2241-2244 (2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings).

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

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.",

keywords = "Gaussian Mixture Model clustering, High-frequency oscillation, Seizure onset zone, Spike, Time-frequency analysis, iEEG",

author = "Su Liu and Zhiyi Sha and Aviva Abosch and Thomas Henry and Ince, {Nuri Firat}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 24th Signal Processing and Communication Application Conference, SIU 2016 ; Conference date: 16-05-2016 Through 19-05-2016",

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doi = "10.1109/SIU.2016.7496221",

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AU - Henry, Thomas

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PY - 2016/6/20

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N2 - 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.

AB - 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.

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

Abstract

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