Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/183985
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dc.coverage.spatialElectronics and Communication Engineering
dc.date.accessioned2017-12-14T11:11:57Z-
dc.date.available2017-12-14T11:11:57Z-
dc.identifier.urihttp://hdl.handle.net/10603/183985-
dc.description.abstractAbstract available
dc.format.extent188 p.
dc.languageEnglish
dc.relation-
dc.rightsuniversity
dc.titleOptimal feature subset selection for pattern classification and recognition of multichannel eeg data using spectral entropy as a complexity measure applications to alcoholic and sleep eeg data
dc.title.alternative-
dc.creator.researcherPadma, T K
dc.subject.keywordFeature
dc.subject.keywordMultichannel
dc.subject.keywordRecognition
dc.description.noteReferences p. 176-188
dc.contributor.guideSriraam, N
dc.publisher.placeKanchipuram
dc.publisher.universitySri Chandrasekharendra Saraswathi Viswa Mahavidyalaya
dc.publisher.institutionDepartment of Electronics and Communications Engineering
dc.date.registeredn.d.
dc.date.completed2016
dc.date.awardedn.d.
dc.format.dimensions-
dc.format.accompanyingmaterialNone
dc.type.degreePh.D.
dc.source.inflibnetINFLIBNET
Appears in Departments:Department of Electronics & Communications Engineering

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01_title page.pdfAttached File30.5 kBAdobe PDFView/Open
02_certificate page.pdf25.11 kBAdobe PDFView/Open
03_acknowledgement.pdf18.42 kBAdobe PDFView/Open
04_abstract.pdf41.76 kBAdobe PDFView/Open
05_table of content.pdf71.25 kBAdobe PDFView/Open
06_abbreviation.pdf30.78 kBAdobe PDFView/Open
07_chapter2.pdf452.31 kBAdobe PDFView/Open
08_chapter3.pdf1.43 MBAdobe PDFView/Open
09_chapter4.pdf615.43 kBAdobe PDFView/Open
10_chapter5.pdf144.6 kBAdobe PDFView/Open
11_chapter6.pdf86.74 kBAdobe PDFView/Open
12_chapter7.pdf600.44 kBAdobe PDFView/Open
13_chapter8.pdf680.58 kBAdobe PDFView/Open
14_chapter9.pdf862.22 kBAdobe PDFView/Open
15_chapter10.pdf609.05 kBAdobe PDFView/Open
16_chapter11.pdf96.11 kBAdobe PDFView/Open
17_reference.pdf93.05 kBAdobe PDFView/Open
18_list of publications.pdf43.66 kBAdobe PDFView/Open


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