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

Content Provider	IEEE Xplore Digital Library
Author	Su Liu Ince, N.F. Abosch, A. Henry, T.R. Zhiyi Sha
Copyright Year	2015
Description	Author affiliation: Univ. of Minnesota, Minneapolis, MN, USA (Henry, T.R.; Zhiyi Sha) \|\| Univ. of Houston, Houston, TX, USA (Su Liu; Ince, N.F.) \|\| Univ. of Colorado, Denver, CO, USA (Abosch, A.)
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.
Starting Page	6602
Ending Page	6605
File Size	1419395
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319906
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hafnium oxide Oscillators Feature extraction Epilepsy Detectors Time-frequency analysis
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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