EEG signal compression based on classified signature and envelope vector sets

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dc.authorid0000-0002-7008-4778
dc.authorid0000-0002-4597-0954
dc.authorid0000-0003-1562-5524
dc.contributor.authorGürkan, Hakanen_US
dc.contributor.authorGüz, Ümiten_US
dc.contributor.authorYarman, Bekir Sıddık Binboğaen_US
dc.date.accessioned2020-03-11T04:27:28Z
dc.date.available2020-03-11T04:27:28Z
dc.date.issued2007
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Electrical-Electronics Engineeringen_US
dc.description.abstractIn this paper, a novel method to compress ElectroEncephaloGram (EEG) Signal is proposed. The proposed method is based on the generation Classified Signature and Envelope Vector Sets (CSEVS) by using an effective k-means clustering algorithm. In this work on a frame basis, any EEG signal is modeled by multiplying three parameters as called the Classified Signature Vector, Classified Envelope Vector, and Frame-Scaling Coefficient. In this case, EEG signal for each frame is described in terms of the two indices R and K of CSEVS and the frame-scaling coefficient. The proposed method is assessed through the use of root-mean-square error (RMSE) and visual inspection measures. The proposed method achieves good compression ratios with low level reconstruction error while preserving diagnostic information in the reconstructed EEG signal.en_US
dc.description.sponsorshipEuropean Circuit Society (ECS)en_US
dc.description.sponsorshipIEEE Circuits and Systems Societyen_US
dc.description.sponsorshipInstitute of Electrical and Electronics Engineering (IEEE)en_US
dc.description.sponsorshipMinisterio de Educacion y Cienciaen_US
dc.description.sponsorshipUniversidad de Sevillaen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationGürkan, H., Güz, Ü. & Yarman, B. S. B. (2007). EEG signal compression based on classified signature and envelope vector sets. Paper presented at the 2007 18th European Conference on Circuit Theory and Design, 420-423. doi:10.1109/ECCTD.2007.4529622en_US
dc.identifier.doi10.1109/ECCTD.2007.4529622
dc.identifier.endpage423
dc.identifier.isbn9781424413416
dc.identifier.isbn9781424413423
dc.identifier.isbn1424413427
dc.identifier.scopus2-s2.0-49749147474
dc.identifier.scopusqualityN/A
dc.identifier.startpage420
dc.identifier.urihttps://hdl.handle.net/11729/2279
dc.identifier.urihttp://dx.doi.org/10.1109/ECCTD.2007.4529622
dc.identifier.wosWOS:000258708400102
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakConference Proceedings Citation Index – Science (CPCI-S)en_US
dc.institutionauthorGürkan, Hakanen_US
dc.institutionauthorGüz, Ümiten_US
dc.institutionauthorid0000-0002-7008-4778
dc.institutionauthorid0000-0002-4597-0954
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEE Computer Societyen_US
dc.relation.ispartof2007 18th European Conference on Circuit Theory and Designen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical signal processingen_US
dc.subjectBrain modelingen_US
dc.subjectCircuit theoryen_US
dc.subjectClassified envelope vectoren_US
dc.subjectClassified signature and envelope vector setsen_US
dc.subjectClassified signature vectoren_US
dc.subjectClustering algorithmsen_US
dc.subjectComputational complexityen_US
dc.subjectComputer scienceen_US
dc.subjectData compressionen_US
dc.subjectDiagnostic informationen_US
dc.subjectEducational institutionsen_US
dc.subjectEEG signal compressionen_US
dc.subjectEEG signalsen_US
dc.subjectElectric variables measurementen_US
dc.subjectElectrocardiographyen_US
dc.subjectElectroencephalogram signalen_US
dc.subjectElectroencephalogram signalsen_US
dc.subjectElectroencephalographyen_US
dc.subjectEnvelope vector setsen_US
dc.subjectError reconstructionen_US
dc.subjectFrame-scaling coefficienten_US
dc.subjectInspectionen_US
dc.subjectK-means clusteringen_US
dc.subjectK-means clustering algorithmen_US
dc.subjectMean square erroren_US
dc.subjectMean square error methodsen_US
dc.subjectMedical signal processingen_US
dc.subjectMonitoringen_US
dc.subjectReconstruction erroren_US
dc.subjectRoot mean square errorsen_US
dc.subjectRoot-mean-square erroren_US
dc.subjectScaling coefficientsen_US
dc.subjectSignal compressionen_US
dc.subjectSignal reconstructionen_US
dc.subjectSignature vectorsen_US
dc.subjectSpeechen_US
dc.subjectThree parametersen_US
dc.subjectVectorsen_US
dc.subjectVisual inspectionen_US
dc.subjectVisual inspection measuresen_US
dc.titleEEG signal compression based on classified signature and envelope vector setsen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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