Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Manylion Llyfryddiaeth
Prif Awduron:	Amiz, Asef Hassan, Talukder, Md. Golam Muid, Shahriar, Labib, Chowdhury, Sahal Ahamad, Hasan, Md. Mehedi
Awduron Eraill:	Parvez, Mohammad Zavid
Fformat:	Traethawd Ymchwil
Iaith:	English
Cyhoeddwyd:	Brac University 2021
Pynciau:	Seizure EEG FFT SVM PSD RBF Support Vector Machine
Mynediad Ar-lein:	http://hdl.handle.net/10361/14981

id	10361-14981
record_format	dspace
spelling	10361-149812022-01-26T10:15:46Z Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals Amiz, Asef Hassan Talukder, Md. Golam Muid Shahriar, Labib Chowdhury, Sahal Ahamad Hasan, Md. Mehedi Parvez, Mohammad Zavid Department of Computer Science and Engineering, Brac University Seizure EEG FFT SVM PSD RBF Support Vector Machine This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-50). Epilepsy is the most common neurological issue in people after stroke. Around 40 or 50 million individuals on the planet endure epilepsy. Epilepsy is characterized by an irregular seizure in which abnormal electrical activity in the mind causes adjusted recognition or conduct. The most commonly used test for detecting Epilepsy is EEG - which stands for Electroencephalogram. In this thesis, we tried to develop an automated system using machine learning that can detect epileptic seizure. We cropped one hour of pre-seizure and post-seizure signal and extracted features from it. We used Fast Fourier Transformation to make our data easier to process and applied Power Spectrum Density (PSD) to calculate energy from it. Finally we used Support Vector Machine (SVM) to classify among these data to differentiate between seizure and non-seizure. We have managed to achieve 89% accuracy using this method on the 23 cases that we had in our dataset. Asef Hassan Amiz Md. Golam Muid Talukder Labib Shahriar Sahal Ahamad Chowdhury Md. Mehedi Hasan B. Computer Science 2021-09-07T09:49:46Z 2021-09-07T09:49:46Z 2021 2021-06 Thesis ID 21141065 ID 16301070 ID 18101704 ID 16301106 ID 16301024 http://hdl.handle.net/10361/14981 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. 50 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Seizure EEG FFT SVM PSD RBF Support Vector Machine
spellingShingle	Seizure EEG FFT SVM PSD RBF Support Vector Machine Amiz, Asef Hassan Talukder, Md. Golam Muid Shahriar, Labib Chowdhury, Sahal Ahamad Hasan, Md. Mehedi Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Parvez, Mohammad Zavid
author_facet	Parvez, Mohammad Zavid Amiz, Asef Hassan Talukder, Md. Golam Muid Shahriar, Labib Chowdhury, Sahal Ahamad Hasan, Md. Mehedi
format	Thesis
author	Amiz, Asef Hassan Talukder, Md. Golam Muid Shahriar, Labib Chowdhury, Sahal Ahamad Hasan, Md. Mehedi
author_sort	Amiz, Asef Hassan
title	Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals
title_short	Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals
title_full	Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals
title_fullStr	Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals
title_full_unstemmed	Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals
title_sort	detection of epileptic seizure using support vector machine classifier - extracted features from eeg signals
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/14981
work_keys_str_mv	AT amizasefhassan detectionofepilepticseizureusingsupportvectormachineclassifierextractedfeaturesfromeegsignals AT talukdermdgolammuid detectionofepilepticseizureusingsupportvectormachineclassifierextractedfeaturesfromeegsignals AT shahriarlabib detectionofepilepticseizureusingsupportvectormachineclassifierextractedfeaturesfromeegsignals AT chowdhurysahalahamad detectionofepilepticseizureusingsupportvectormachineclassifierextractedfeaturesfromeegsignals AT hasanmdmehedi detectionofepilepticseizureusingsupportvectormachineclassifierextractedfeaturesfromeegsignals
_version_	1814308230700990464

Detection of epileptic seizure using Support Vector Machine Classifier - extracted features from EEG signals

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