A new approach towards predictability of epileptic seizures: KLT dimension.

Full Abstract

This paper proposes a measure of complexity of the epileptic electroencephalogram (EEG) based on the dimensionality of the Karhunen-Loeve Transform (KLT) in the time domain. We estimate the KLT dimensionality by assuming the same observation noise level in the EEG during the interictal period (between the seizures) as the one during an epileptic seizure (ictal period). Utilizing an optimality criterion based on the T-index [1] and the predictability time, derived from the created KLT dimensionality profiles, we show that 10 out of 15 seizures in one patient with temporal lobe epilepsy were predictable with an average predictability time of about 36 minutes.

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Author information

Author/s: Venugopal, Rajeshkumar (R); Narayanan, K (K); Prasad, Awadhesh (A); Spanias, A (A); Sackellares, J C (JC); Iasemidis, L D (LD);

Affiliation: Department of Electrical Engineering, Arizona State University, Tempe, AZ 85287, USA.

Grants: NS039687 (Agency:United States NINDS)

Journal and publication information

Publication Type: Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.

Journal: Biomedical sciences instrumentation (Biomed Sci Instrum), published in United States. (Language: eng)

Reference: 2003-; vol 39 (issue ) : pp 123-8

Dates: Created 2003/05/02; Completed 2003/06/17; Revised 2007/11/14;

PMID: 12724880, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Brain - physiopathology
Brain Mapping - methods
Cerebral Cortex - physiopathology
Electroencephalography - methods
Epilepsy - diagnosis; physiopathology
Hippocampus - physiopathology

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