Detection of high frequency oscillations in epilepsy with k-means clustering method

Su Liu, Nuri F. Ince, Akin Sabanci, Aydin Aydoseli, Yavuz Aras, Altay Sencer, Nerses Bebek, Zhiyi Sha, Candan Gurses

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

7 Scopus citations

Abstract

High frequency oscillations (HFOs) have been considered as a promising clinical biomarker of epileptogenic regions in brain. Due to their low amplitude, short duration, and variability in patterns, the visual identification of HFOs in long-term continuous intracranial EEG (iEEG) is cumbersome. The aim of our study is to improve and automatize the detection of HFO patterns by developing analysis tools based on an unsupervised k-means clustering method exploring the time-frequency content of iEEG. The clustering approach successfully isolated HFOs from noise, artifacts, and arbitrary spikes. We tested this technique on three subjects. Using this algorithm we were able to localize the seizure onset area in all of the subjects. The channel with maximum number of HFOs was associated with the seizure onset.

Original languageEnglish (US)
Title of host publication2015 7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
PublisherIEEE Computer Society
Pages934-937
Number of pages4
ISBN (Electronic)9781467363891
DOIs
StatePublished - Jul 1 2015
Event7th International IEEE/EMBS Conference on Neural Engineering, NER 2015 - Montpellier, France
Duration: Apr 22 2015Apr 24 2015

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2015-July
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other7th International IEEE/EMBS Conference on Neural Engineering, NER 2015
CountryFrance
CityMontpellier
Period4/22/154/24/15

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