New approach in features extraction for EEG signal detection

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dc.contributor.author Guerrero Mosquera, Carlos Andrés
dc.contributor.author Navia Vázquez, Ángel
dc.date.accessioned 2010-07-19T09:26:57Z
dc.date.available 2010-07-19T09:26:57Z
dc.date.issued 2009-11
dc.identifier.bibliographicCitation Proceedings 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, p. 13-16
dc.identifier.isbn 978-1-4244-3296-7
dc.identifier.issn 1557-170X
dc.identifier.uri http://hdl.handle.net/10016/9098
dc.description 4 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.abstract This 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.sponsorship This work has been funded by the Spain CICYT grant TEC2008-02473.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights © IEEE
dc.rights © Engineering in Medicine and Biology Society (EMBS)
dc.subject.other EEG signal detection
dc.subject.other McAulay-Quatieri sinusoidal model
dc.subject.other Abnormal neural discharges
dc.subject.other Electroencephalogram
dc.subject.other Epileptic seizures
dc.subject.other Feature extraction
dc.subject.other Smoothed pseudo Wigner-Ville distribution
dc.subject.other Time-frequency distributions
dc.title New approach in features extraction for EEG signal detection
dc.type conferenceObject
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.relation.publisherversion http://dx.doi.org/10.1109/IEMBS.2009.5332434
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi 10.1109/IEMBS.2009.5332434
dc.rights.accessRights openAccess
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