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Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings

McSharry, Patrick E., He, T., Smith, Leonard A. and Tarassenko, L. (2002) Linear and non-linear methods for automatic seizure detection in scalp electro-encephalogram recordings. Medical & Biological Engineering & Computing, 40 (4). pp. 447-461. ISSN 0140-0118

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Abstract

The electro-encephalogram is a time-varying signal that measures electrical activity in the brain. A conceptually intuitive non-linear technique, multi- dimensional probability evolution (MDPE), is introduced. It is based on the time evolution of the probability density function within a multi-dimensional state space. A synthetic recording is employed to illustrate why MDPE is capable of detecting changes in the underlying dynamics that are invisible to linear statistics. If a non- linear statistic cannot outperform a simple linear statistic such as variance, then there is no reason to advocate its use. Both variance and MDPE were able to detect the seizure in each of the ten scalp EEG recordings investigated. Although MDPE produced fewer false positives, there is no firm evidence to suggest that MDPE, or any other non-linear statistic considered, outperforms variance-based methods at identifying seizures.

Item Type: Article
Official URL: http://link.springer.com/journal/11517
Additional Information: © 2002 Springer
Library of Congress subject classification: Q Science > QA Mathematics
R Medicine > R Medicine (General)
Sets: Departments > Statistics
Research centres and groups > Centre for the Analysis of Time Series (CATS)
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 05 Mar 2014 10:54
URL: http://eprints.lse.ac.uk/55995/

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