NDLI: Does the morphology of high-frequency (100–500 Hz) brain oscillations change during epileptic seizures?

Content Provider	IEEE Xplore Digital Library
Author	Pearce, A. Wulsin, D. Litt, B. Blanco, J.
Copyright Year	2011
Description	Author affiliation: University of Pennsylvania, Philadelphia, 19104, USA (Pearce, A.; Wulsin, D.; Litt, B.) \|\| United States Naval Academy, Annapolis, MD 21402, USA (Blanco, J.)
Abstract	Transient high-frequency (100–500 Hz) oscillations (HFOs) recorded directly from the surface of the human brain are emerging as a potential biomarker for epileptogenic brain tissue. Whether the morphology of these events can be used to understand the process of seizure generation is unknown. In this experiment, we used supervised learning techniques in an attempt to distinguish HFOs occurring during versus outside of seizures in five patients implanted with intracranial micro-and macroelectrodes as part of routine evaluation for epilepsy surgery. We trained three classifiers using logistic regression, k-nearest neighbors, and support vector machines, respectively, and assessed their performance using the F1 measure in conjunction with permutation testing. All of the classifiers produced a low number of true positives relative to false positives and false negatives, but two of the classifiers performed slightly better than chance in certain patients. These results suggest that ictal HFOs are difficult to distinguish from those occurring interictally, and that a marked change in HFO morphology is not likely to be associated with seizure generation.
Starting Page	461
Ending Page	465
File Size	208852
Page Count	5
File Format	PDF
ISBN	9781467303217
ISSN	10586393
e-ISBN	9781467303231
DOI	10.1109/ACSSC.2011.6190042
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hafnium compounds Support vector machines Electrodes Epilepsy Oscillators Humans Aggregates
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Networks and Communications

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