NDLI: Rapid identification of epileptogenic sites in the intracranial EEG

Content Provider	IEEE Xplore Digital Library
Author	Yadav, R. Swamy, M.N.S. Agarwal, R.
Copyright Year	2011
Description	Author affiliation: Center for Signal Processing and Communications (CENS1PCOM), Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. West, Montreal, QC, H3G 1M8, Canada (Yadav, R.; Swamy, M.N.S.; Agarwal, R.)
Abstract	The paper presents a novel computationally simple, easy-to-interpret compressed EEG display for multichannel intracranial EEG recordings. The compressed display is based on the level of sharp activity (relative sharpness index (RSI)) in the EEG, which profoundly increases during paroxysmal activities. RSI is graphically presented as a color-intensity plot that allows compressing several hours of EEG into a single display page. RSI display is a bird's-eye-view of the EEG that may reveal seizure evolution ('build-up'), seizure precursors, or sites associated with the seizures. We present examples from two patients to illustrate the method's ability to identify epileptogenic sites that may be difficult to observe in the conventional review process. RSI is compared with the color density spectral array (CDSA) and amplitude integrated EEG (aEEG) display. Examples demonstrate the RSI display to be simple, easy to interpret, computationally light and fast enough for online application.
Starting Page	7553
Ending Page	7556
File Size	1183037
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091862
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	USA Councils Conferences aEEG EEG seizure detection compressed EEG display CDSA
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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