Feature extraction of EEG signals for seizure detection using machine learning algorithms

Other Title(s)

استخراج السمات من إشارات المخطط الكهربائي لدماغ لاكتشاف النوبات باستخدام التعلم الآلي

Dissertant

al-Maraziqah, al-Nawras Mani Muhammad

Thesis advisor

Balasi, Anas Hasan Sulayman

University

Mutah University

Faculty

Information Technology College

Department

Computer Science Department

University Country

Jordan

Degree

Master

Degree Date

2020

English Abstract

Epilepsy is a central nervous system (neurological) disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior, sensations, and sometimes loss of awareness.

Anyone can develop epilepsy.

Epilepsy affects both males and females of all races, ethnic backgrounds, and ages.

detecting seizures is a challenge due to difference in humans’ behaviors and brains signals.

The used technology affect detecting the seizures too.

This thesis Aims is to have an overview over the last few years of the wide variety of such approaches based on the taxonomy of statistics and machine learning classification with several types of classifiers.

Four Feature extraction Algorithms Min-Max feature extraction (MniMx), Wavelet Packet Decomposition (WPD), Wavelet Filter Bank (WFB), and Genetic Algorithm-Based Frequency-Domain Feature Search (GAFDS) have been used for supporting the classifier results, the PCA select the best liner results from the four-feature extraction.

Convolution Neural Network (CNN), Decision tree (DT), Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN), have been used to classify and predict Epilepsy using the data that been extracted from the CHB-MIT dataset.

The results from the classifiers show a promising accuracy rate using CNN as a classifier and GAFDS as feature extraction with reaching (97%) and the lowest accuracy rate for the decision tree reaching (84%).

Main Subjects

Information Technology and Computer Science

No. of Pages

74

Table of Contents

Table of contents.

Abstract.

Abstract in Arabic.

Chapter One : Introduction.

Chapter Two : Literature review.

Chapter Three : Research methodology.

Chapter Four : Implementation and experiment work.

References.

American Psychological Association (APA)

al-Maraziqah, al-Nawras Mani Muhammad. (2020). Feature extraction of EEG signals for seizure detection using machine learning algorithms. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-1340209

Modern Language Association (MLA)

al-Maraziqah, al-Nawras Mani Muhammad. Feature extraction of EEG signals for seizure detection using machine learning algorithms. (Master's theses Theses and Dissertations Master). Mutah University. (2020).
https://search.emarefa.net/detail/BIM-1340209

American Medical Association (AMA)

al-Maraziqah, al-Nawras Mani Muhammad. (2020). Feature extraction of EEG signals for seizure detection using machine learning algorithms. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-1340209

Language

English

Data Type

Arab Theses

Record ID

BIM-1340209