Epileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approaches

This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.

Bibliografske podrobnosti
Main Authors:	Karim, Rezwanul, Nitol, Subah, Rahman, Md.Mushfiqur
Drugi avtorji:	Alam, Md.Ashraful
Format:	Thesis
Jezik:	English
Izdano:	BRAC University 2019
Teme:	EEG Epilepsy Seizure SVM STFT PWD Machine leaning
Online dostop:	http://hdl.handle.net/10361/11442

id	10361-11442
record_format	dspace
spelling	10361-114422022-01-26T10:10:29Z Epileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approaches Karim, Rezwanul Nitol, Subah Rahman, Md.Mushfiqur Alam, Md.Ashraful Parvez, Mohammad Zavid Department of Computer Science and Engineering, BRAC University EEG Epilepsy Seizure SVM STFT PWD Machine leaning This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Includes bibliographical references (pages 40-45). Cataloged from PDF version of thesis. In 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. Rezwanul Karim Subah Nitol Md.Mushfiqur Rahman B. Computer Science and Engineering 2019-02-20T09:14:48Z 2019-02-20T09:14:48Z 2018 2018-12 Thesis ID 14301038 ID 14301116 ID 14301130 http://hdl.handle.net/10361/11442 en BRAC 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. 45 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	EEG Epilepsy Seizure SVM STFT PWD Machine leaning
spellingShingle	EEG Epilepsy Seizure SVM STFT PWD Machine leaning Karim, Rezwanul Nitol, Subah Rahman, Md.Mushfiqur Epileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approaches
description	This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
author2	Alam, Md.Ashraful
author_facet	Alam, Md.Ashraful Karim, Rezwanul Nitol, Subah Rahman, Md.Mushfiqur
format	Thesis
author	Karim, Rezwanul Nitol, Subah Rahman, Md.Mushfiqur
author_sort	Karim, Rezwanul
title	Epileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approaches
title_short	Epileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approaches
title_full	Epileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approaches
title_fullStr	Epileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approaches
title_full_unstemmed	Epileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approaches
title_sort	epileptic seizure detection by exploiting eeg signals using different decomposition techniques and machine learning approaches
publisher	BRAC University
publishDate	2019
url	http://hdl.handle.net/10361/11442
work_keys_str_mv	AT karimrezwanul epilepticseizuredetectionbyexploitingeegsignalsusingdifferentdecompositiontechniquesandmachinelearningapproaches AT nitolsubah epilepticseizuredetectionbyexploitingeegsignalsusingdifferentdecompositiontechniquesandmachinelearningapproaches AT rahmanmdmushfiqur epilepticseizuredetectionbyexploitingeegsignalsusingdifferentdecompositiontechniquesandmachinelearningapproaches
_version_	1814307784611594240

Epileptic seizure detection by exploiting EEG signals using different decomposition techniques and machine learning approaches

Podobne knjige/članki