Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/29063
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dc.coverage.spatialComputer Scienceen_US
dc.date.accessioned2014-11-26T08:06:05Z-
dc.date.available2014-11-26T08:06:05Z-
dc.date.issued2014-11-26-
dc.identifier.urihttp://hdl.handle.net/10603/29063-
dc.description.abstractNone newlineen_US
dc.format.extentxii, 145p.en_US
dc.languageEnglishen_US
dc.relationno. of references 121en_US
dc.rightsuniversityen_US
dc.titleEfficient Arrhthmia Classification in ECG using Machine Learning Techniquesen_US
dc.title.alternativenilen_US
dc.creator.researcherKarpagachelvi, Sen_US
dc.subject.keywordcomputer, arrthmia, ECG, machine, techniquesen_US
dc.description.notereference p. 133 -145en_US
dc.contributor.guideArthanariee, A Men_US
dc.publisher.placeKodaikanalen_US
dc.publisher.universityMother Teresa Womens Universityen_US
dc.publisher.institutionDepartment of Computer Scienceen_US
dc.date.registered01.11.2006en_US
dc.date.completed27.06.2013en_US
dc.date.awarded28.01.2014en_US
dc.format.dimensionsA4en_US
dc.format.accompanyingmaterialCDen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Department of Computer Science

Files in This Item:
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01_title.pdfAttached File42.15 kBAdobe PDFView/Open
02_certificate.pdf20.33 kBAdobe PDFView/Open
03_declaration.pdf20.64 kBAdobe PDFView/Open
04_acknowledgement.pdf23.68 kBAdobe PDFView/Open
05_contents.pdf31.56 kBAdobe PDFView/Open
06_list_of_tables.pdf21.72 kBAdobe PDFView/Open
07_list_of_figures.pdf25.65 kBAdobe PDFView/Open
08_abbreviations.pdf25.76 kBAdobe PDFView/Open
09_chapter1.pdf1.94 MBAdobe PDFView/Open
10_chapter2.pdf124.92 kBAdobe PDFView/Open
11_chapter3.pdf5.24 MBAdobe PDFView/Open
12_chapter4.pdf3.49 MBAdobe PDFView/Open
13_chapter5.pdf1.62 MBAdobe PDFView/Open
14_chapter6.pdf601.81 kBAdobe PDFView/Open
15_chapter7.pdf442.15 kBAdobe PDFView/Open
16_chapter8.pdf51.19 kBAdobe PDFView/Open
17_conclusion.pdf18.9 kBAdobe PDFView/Open
18_bibliography.pdf104.08 kBAdobe PDFView/Open


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