TY - GEN
T1 - Identification of seizure onset zone using automatically detected spike and high-frequency oscillation in human intracranial EEG
AU - Liu, Su
AU - Sha, Zhiyi
AU - Abosch, Aviva
AU - Henry, Thomas
AU - Ince, Nuri Firat
PY - 2016/6/20
Y1 - 2016/6/20
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.
KW - Gaussian Mixture Model clustering
KW - High-frequency oscillation
KW - Seizure onset zone
KW - Spike
KW - Time-frequency analysis
KW - iEEG
UR - http://www.scopus.com/inward/record.url?scp=84982856628&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84982856628&partnerID=8YFLogxK
U2 - 10.1109/SIU.2016.7496221
DO - 10.1109/SIU.2016.7496221
M3 - Conference contribution
AN - SCOPUS:84982856628
T3 - 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings
SP - 2241
EP - 2244
BT - 2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th Signal Processing and Communication Application Conference, SIU 2016
Y2 - 16 May 2016 through 19 May 2016
ER -