Investigation of automatically detected high frequency oscillations (HFOs) as an early predictor of seizure onset zone

Su Liu, Nuri F. Ince, Aviva Abosch, Thomas R. Henry, Zhiyi Sha

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

1 Scopus citations

Abstract

High frequency oscillations (HFOs) during inter-ictal state have been considered as a potential biomarker of epileptogenic regions in brain. The purpose of the current study is to improve and automatize the detection of HFOs basing on HFO distinguishing features followed by unsupervised clustering method, and to predict seizure onset zone (SOZ) using the clustered HFOs. The algorithm successfully separated HFOs of different sub-categories from noise, artifacts, and inter-ictal spikes. We tested this technique on two subjects, and assessed the performance of SOZ prediction by computing the overlapping rate of HFO generative channels and seizure onset channels. In both subjects, we were able to localize the seizure onset area 3 to 4 days before the actual onset of the seizure, with high specificity over 95%. The algorithm showed significant improvement comparing to another existing technique.

Original languageEnglish (US)
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6602-6605
Number of pages4
ISBN (Electronic)9781424492718
DOIs
StatePublished - Nov 4 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2015-November
ISSN (Print)1557-170X

Other

Other37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/TerritoryItaly
CityMilan
Period8/25/158/29/15

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