Electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease

Epilepsy is a brain disease caused by abnormal brain activities. Machine learning algorithms are usually applied in the classification and identification of epilepsy at an early stage. This study's primary objective is to classify the Electroencephalography (EEG) signal dataset of epileptic sei...

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Main Author:	Abdul Aziz, Nur Syahirah
Format:	Thesis
Language:	English
Published:	2022
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/102291/1/NurSyahirahAbdulAzizMFS2022.pdf.pdf
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id	my-utm-ep.102291
record_format	uketd_dc
spelling	my-utm-ep.1022912023-08-17T00:35:21Z Electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease 2022 Abdul Aziz, Nur Syahirah QA Mathematics Epilepsy is a brain disease caused by abnormal brain activities. Machine learning algorithms are usually applied in the classification and identification of epilepsy at an early stage. This study's primary objective is to classify the Electroencephalography (EEG) signal dataset of epileptic seizures using a machine learning algorithm and to evaluate the performance using the Plot Confusion Matrix and area under the receiver operating characteristic (AUC-ROC) curve. The plot confusion matrix method will give an array that depicts the True Positives, False Positives, False Negatives, and True Negatives. Besides, the AUC-ROC curve is a performance measurement for classification problems at various threshold settings. These methods can be used to check or visualize the performance of the multi-class classification problem. This thesis involves a collection of datasets containing 200 healthy individuals and 300 epilepsy patients. Next, features were extracted from these datasets. Feature extractions help to reduce data dimensionality and eliminate noise, while its output is used as the input for classifier-based epileptic classification. This study selected the Discrete Wavelet Transform (DWT) and Statistical Features as feature extraction methods. In addition, multiple machine learning techniques are presented in this study. Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Decision Tree, and Ensemble Bagged Tree (EBT) were used as classifiers. Furthermore, Linear Discriminant Analysis (LDA) has been selected as the benchmark standard. Therefore, five classifiers were trained for classification purposes. Each classifier is combined with DWT and Statistical Features. The proposed feature extraction, DWT combined with BPNN, gives the highest accuracy of 91.2% and a shorter duration of training. 2022 Thesis http://eprints.utm.my/id/eprint/102291/ http://eprints.utm.my/id/eprint/102291/1/NurSyahirahAbdulAzizMFS2022.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149135 masters Universiti Teknologi Malaysia Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Abdul Aziz, Nur Syahirah Electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease
description	Epilepsy is a brain disease caused by abnormal brain activities. Machine learning algorithms are usually applied in the classification and identification of epilepsy at an early stage. This study's primary objective is to classify the Electroencephalography (EEG) signal dataset of epileptic seizures using a machine learning algorithm and to evaluate the performance using the Plot Confusion Matrix and area under the receiver operating characteristic (AUC-ROC) curve. The plot confusion matrix method will give an array that depicts the True Positives, False Positives, False Negatives, and True Negatives. Besides, the AUC-ROC curve is a performance measurement for classification problems at various threshold settings. These methods can be used to check or visualize the performance of the multi-class classification problem. This thesis involves a collection of datasets containing 200 healthy individuals and 300 epilepsy patients. Next, features were extracted from these datasets. Feature extractions help to reduce data dimensionality and eliminate noise, while its output is used as the input for classifier-based epileptic classification. This study selected the Discrete Wavelet Transform (DWT) and Statistical Features as feature extraction methods. In addition, multiple machine learning techniques are presented in this study. Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Decision Tree, and Ensemble Bagged Tree (EBT) were used as classifiers. Furthermore, Linear Discriminant Analysis (LDA) has been selected as the benchmark standard. Therefore, five classifiers were trained for classification purposes. Each classifier is combined with DWT and Statistical Features. The proposed feature extraction, DWT combined with BPNN, gives the highest accuracy of 91.2% and a shorter duration of training.
format	Thesis
qualification_level	Master's degree
author	Abdul Aziz, Nur Syahirah
author_facet	Abdul Aziz, Nur Syahirah
author_sort	Abdul Aziz, Nur Syahirah
title	Electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease
title_short	Electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease
title_full	Electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease
title_fullStr	Electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease
title_full_unstemmed	Electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease
title_sort	electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Science
publishDate	2022
url	http://eprints.utm.my/id/eprint/102291/1/NurSyahirahAbdulAzizMFS2022.pdf.pdf
_version_	1776100888849416192

Electroencephalography signal classification using neural network, decision tree and ensemble bagged tree for epilepsy disease

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