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New approach in features extraction for EEG signal detection

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.contributor.authorGuerrero Mosquera, Carlos Andrés
dc.contributor.authorNavia Vázquez, Ángel
dc.date.accessioned2010-07-19T09:26:57Z
dc.date.available2010-07-19T09:26:57Z
dc.date.issued2009-11
dc.description4 pages, 3 figures.-- Contributed to: "Engineering the Future of Biomedicine", EMBC2009, 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Minneapolis, Minnesota, USA, Sep 2-6, 2009).
dc.description.abstractThis paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the Smoothed Pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature based on the length of the track that, combined with energy and frequency features, allows to isolate a continuous energy trace from another oscillations when an epileptic seizure is beginning. We evaluate our approach using data consisting of 16 different seizures from 6 epileptic patients. The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs.
dc.description.sponsorshipThis work has been funded by the Spain CICYT grant TEC2008-02473.
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationProceedings 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, p. 13-16
dc.identifier.doi10.1109/IEMBS.2009.5332434
dc.identifier.isbn978-1-4244-3296-7
dc.identifier.issn1557-170X
dc.identifier.urihttps://hdl.handle.net/10016/9098
dc.language.isoeng
dc.publisherIEEE
dc.relation.publisherversionhttp://dx.doi.org/10.1109/IEMBS.2009.5332434
dc.rights© IEEE
dc.rights© Engineering in Medicine and Biology Society (EMBS)
dc.rights.accessRightsopen access
dc.subject.ecienciaTelecomunicaciones
dc.subject.otherEEG signal detection
dc.subject.otherMcAulay-Quatieri sinusoidal model
dc.subject.otherAbnormal neural discharges
dc.subject.otherElectroencephalogram
dc.subject.otherEpileptic seizures
dc.subject.otherFeature extraction
dc.subject.otherSmoothed pseudo Wigner-Ville distribution
dc.subject.otherTime-frequency distributions
dc.titleNew approach in features extraction for EEG signal detection
dc.typeconference paper*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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