Manifold Learning Applied on EEG Signal of the Epileptic Patients for Detection of Normal and Pre-Seizure States
In this paper, several manifold learning (ML) techniques for dimension reduction of EEG feature vectors are introduced and applied on set of epileptic EEG signals. These include principal component analysis (PCA), multidimensional scaling (MDS), isometric mapping (ISOMAP) and locally linear embeddin...
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| creator | Ataee, P. Yazdani, A. Setarehdan, S.K. Noubari, H.A. |
| description | In this paper, several manifold learning (ML) techniques for dimension reduction of EEG feature vectors are introduced and applied on set of epileptic EEG signals. These include principal component analysis (PCA), multidimensional scaling (MDS), isometric mapping (ISOMAP) and locally linear embedding (LLE). While EEG signals of epileptic patients contain necessary information with regards to the various brain states of epileptic patients, for extraction of useful information in the EEG signals and for detection, often construction of high-dimensional feature vectors is utilized. Analysis of such high-dimensional feature vectors are complex and time consuming. This paper deals with dimension reduction of the extracted feature vectors and comparative analysis of the performance of several manifold learning techniques as applied on EEG signals of epileptic patients. |
| doi_str_mv | 10.1109/IEMBS.2007.4353588 |
| format | Conference Proceeding |
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Yazdani, A. ; Setarehdan, S.K. ; Noubari, H.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i301t-94fa672affd08993c8796034fa59237a6bf9f2054c84c76a872f75bbb8250bdf3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Data mining</topic><topic>Diagnosis, Computer-Assisted - methods</topic><topic>Electroencephalography</topic><topic>Electroencephalography - methods</topic><topic>Epilepsy</topic><topic>Feature extraction</topic><topic>Humans</topic><topic>Multidimensional systems</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Performance analysis</topic><topic>Principal component analysis</topic><topic>Reference Values</topic><topic>Reproducibility of Results</topic><topic>Seizures - diagnosis</topic><topic>Sensitivity and Specificity</topic><topic>Signal analysis</topic><topic>Signal detection</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Ataee, P.</creatorcontrib><creatorcontrib>Yazdani, A.</creatorcontrib><creatorcontrib>Setarehdan, S.K.</creatorcontrib><creatorcontrib>Noubari, H.A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ataee, P.</au><au>Yazdani, A.</au><au>Setarehdan, S.K.</au><au>Noubari, H.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Manifold Learning Applied on EEG Signal of the Epileptic Patients for Detection of Normal and Pre-Seizure States</atitle><btitle>2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society</btitle><stitle>IEMBS</stitle><addtitle>Conf Proc IEEE Eng Med Biol Soc</addtitle><date>2007-01-01</date><risdate>2007</risdate><volume>2007</volume><spage>5489</spage><epage>5492</epage><pages>5489-5492</pages><issn>1094-687X</issn><issn>1557-170X</issn><eissn>1558-4615</eissn><isbn>9781424407873</isbn><isbn>1424407877</isbn><eisbn>9781424407880</eisbn><eisbn>1424407885</eisbn><abstract>In this paper, several manifold learning (ML) techniques for dimension reduction of EEG feature vectors are introduced and applied on set of epileptic EEG signals. 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| identifier | ISSN: 1094-687X |
| ispartof | 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, Vol.2007, p.5489-5492 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithms Artificial Intelligence Data mining Diagnosis, Computer-Assisted - methods Electroencephalography Electroencephalography - methods Epilepsy Feature extraction Humans Multidimensional systems Pattern Recognition, Automated - methods Performance analysis Principal component analysis Reference Values Reproducibility of Results Seizures - diagnosis Sensitivity and Specificity Signal analysis Signal detection Vectors |
| title | Manifold Learning Applied on EEG Signal of the Epileptic Patients for Detection of Normal and Pre-Seizure States |
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