Optimal feature selection for seizure detection: A subspace based approach

Tolga E. Özkurt*, Mingui Sun, Tayfun Akgül, Robert J. Sclabassi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

An epileptic seizure detector's performance definitely depends on features extraction and selection. In this study, we present the short-time average magnitude difference function (sAMDF) as a computationally efficient feature to distinguish seizures from EEG and it is compared with the frequently used curve length. We also suggest using a subspace based approach for feature selection that exploits divergence measure as the dissimilarity criterion. In this approach, basically features are linearly transformed into another reduced space for optimality while decreasing the computational burden. Seizure discrimination performances of transformed features and original features are compared. The obtained results demonstrate that the feature selection with a divergence-based subspace approach is quite useful to discriminate the seizure parts of the signal from the nonseizure ones.

Original languageEnglish
Title of host publication28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Pages2134-2137
Number of pages4
DOIs
Publication statusPublished - 2006
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: 30 Aug 20063 Sept 2006

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ISSN (Print)0589-1019

Conference

Conference28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Country/TerritoryUnited States
CityNew York, NY
Period30/08/063/09/06

Funding

FundersFunder number
National Institute of Biomedical Imaging and BioengineeringR01EB002309

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