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dc.contributor.advisorRahman, Dr. Mohammad Zahidur
dc.contributor.authorRahman, Md. Oliur
dc.contributor.authorKarim, Md. Naushad
dc.date.accessioned2015-09-03T09:59:24Z
dc.date.available2015-09-03T09:59:24Z
dc.date.copyright2015
dc.date.issued2015-08-24
dc.identifier.otherID 12101120
dc.identifier.otherID 14241012
dc.identifier.urihttp://hdl.handle.net/10361/4378
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.en_US
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 48-53).
dc.description.abstractEpilepsy is the most common chronic disease which involves 1% of the population in the world. It is a neurological disorder characterized by irregular brain tissue activity causing seizure. For many patients mental stress not knowing when seizure occurs is more than having the disease. In some cases physicians inspect lengthy EEG data visually for seizure prediction which involves a lot of time and sometimes tend towards faulty diagnosis. But decision support systems are used since 1960 by many physicians and in this paper we are trying to apply modern signal processing and machine learning techniques to improve the accuracy of these decision support systems. Hilbert Huang Transform (HHT) is a very new and powerful tool for analyzing data from non-stationary and nonlinear processing realm and capable of filtering data based on empirical mode decomposition (EMD). The EMD is based on the sequential extraction of energy associated with various intrinsic time scales of the signal; therefore total sum of the intrinsic mode functions (IMFs) matches the signal very well and ensures completeness. Artificial neural networks (ANNs) offer many potentially superior method of EEG signal analysis to the spectral analysis methods. In contrast to the conventional spectral analysis methods, ANNs not only model the signal, but also make a decision as to the class of signal [26–29]. Feed-forward neural networks are a basic type of ANNs capable of approximating generic classes of functions, including continuous and integrable ones. An important class of feed-forward neural networks is multilayer perceptron neural networks (MLPNNs). In this paper we proposed a model for predicting epileptic seizure using EMD for features extraction and MLPNN for classification.en_US
dc.description.statementofresponsibilityMd. Oliur Rahman
dc.description.statementofresponsibilityMd. Naushad Karim
dc.format.extent53 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis 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.subjectComputer science and engineeringen_US
dc.subjectEpilepsyen_US
dc.subjectHilbert Huang Transform (HHT)en_US
dc.titlePredicting epileptic seizure from Electroencephalography (EEG) using hilbert huang transformation and neural networken_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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