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dc.contributor.advisorAlam, Md.Ashraful
dc.contributor.advisorParvez, Mohammad Zavid
dc.contributor.authorKarim, Rezwanul
dc.contributor.authorNitol, Subah
dc.contributor.authorRahman, Md.Mushfiqur
dc.date.accessioned2019-02-20T09:14:48Z
dc.date.available2019-02-20T09:14:48Z
dc.date.copyright2018
dc.date.issued2018-12
dc.identifier.otherID 14301038
dc.identifier.otherID 14301116
dc.identifier.otherID 14301130
dc.identifier.urihttp://hdl.handle.net/10361/11442
dc.descriptionThis thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.en_US
dc.descriptionIncludes bibliographical references (pages 40-45).
dc.descriptionCataloged from PDF version of thesis.
dc.description.abstractIn recent years, detecting epileptic seizure has gained a high demand in the field of research. It is such a common and high talked brain disorder, since more than 65 million individuals worldwide are affected by this very disease. Electroencephalogram (EEG) signals is widely used for identifying brain diseases like epileptic seizure. In this thesis, two features are extracted based on short-time fourier transform(STFT) and pseudo-wigner distribution (PWD) and these features are then used to classify seizure and non-seizure EEG signals using support vector machine (SVM). Experimental results show that our proposed approach achieved high classification accuracy (i.e.,92.4%) considering five groups of people. Key-words: EEG, Epilepsy, Seizure, SVM, STFT, PWD.en_US
dc.description.statementofresponsibilityRezwanul Karim
dc.description.statementofresponsibilitySubah Nitol
dc.description.statementofresponsibilityMd.Mushfiqur Rahman
dc.format.extent45 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectEEGen_US
dc.subjectEpilepsyen_US
dc.subjectSeizureen_US
dc.subjectSVMen_US
dc.subjectSTFTen_US
dc.subjectPWDen_US
dc.subject.lcshMachine leaning
dc.titleEpileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approachesen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, BRAC University
dc.description.degreeB. Computer Science and Engineering


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