Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification

Automated analysis of brain activity from electroencephalogram (EEG) has indispensable applications in many fields such as epilepsy research. This research has studied the abilities of negative selection and clonal selection in artificial immune system (AIS) and particle swarm optimization (PSO) to...

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Main Author:	Sahel Ba-Karait, Nasser Omer
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/77766/1/NasserOmerSahelPFC2015.pdf
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spelling	my-utm-ep.777662018-07-04T11:44:17Z Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification 2015-11 Sahel Ba-Karait, Nasser Omer QA75 Electronic computers. Computer science Automated analysis of brain activity from electroencephalogram (EEG) has indispensable applications in many fields such as epilepsy research. This research has studied the abilities of negative selection and clonal selection in artificial immune system (AIS) and particle swarm optimization (PSO) to produce different reliable and efficient methods for EEG-based epileptic seizure recognition which have not yet been explored. Initially, an optimization-based classification model was proposed to describe an individual use of clonal selection and PSO to build nearest centroid classifier for EEG signals. Next, two hybrid optimization-based negative selection models were developed to investigate the integration of the AIS-based techniques and negative selection with PSO from the perspective of classification and detection. In these models, a set of detectors was created by negative selection as self-tolerant and their quality was improved towards non-self using clonal selection or PSO. The models included a mechanism to maintain the diversity and generality among the detectors. The detectors were produced in the classification model for each class, while the detection model generated the detectors only for the abnormal class. These hybrid models differ from each other in hybridization configuration, solution representation and objective function. The three proposed models were abstracted into innovative methods by applying clonal selection and PSO for optimization, namely clonal selection classification algorithm (CSCA), particle swarm classification algorithm (PSCA), clonal negative selection classification algorithm (CNSCA), swarm negative selection classification algorithm (SNSCA), clonal negative selection detection algorithm (CNSDA) and swarm negative selection detection algorithm (SNSDA). These methods were evaluated on EEG data using common measures in medical diagnosis. The findings demonstrated that the methods can efficiently achieve a reliable recognition of epileptic activity in EEG signals. Although CNSCA gave the best performance, CNSDA and SNSDA are preferred due to their efficiency in time and space. A comparison with other methods in the literature showed the competitiveness of the proposed methods. 2015-11 Thesis http://eprints.utm.my/id/eprint/77766/ http://eprints.utm.my/id/eprint/77766/1/NasserOmerSahelPFC2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:96443 phd doctoral Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Sahel Ba-Karait, Nasser Omer Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification
description	Automated analysis of brain activity from electroencephalogram (EEG) has indispensable applications in many fields such as epilepsy research. This research has studied the abilities of negative selection and clonal selection in artificial immune system (AIS) and particle swarm optimization (PSO) to produce different reliable and efficient methods for EEG-based epileptic seizure recognition which have not yet been explored. Initially, an optimization-based classification model was proposed to describe an individual use of clonal selection and PSO to build nearest centroid classifier for EEG signals. Next, two hybrid optimization-based negative selection models were developed to investigate the integration of the AIS-based techniques and negative selection with PSO from the perspective of classification and detection. In these models, a set of detectors was created by negative selection as self-tolerant and their quality was improved towards non-self using clonal selection or PSO. The models included a mechanism to maintain the diversity and generality among the detectors. The detectors were produced in the classification model for each class, while the detection model generated the detectors only for the abnormal class. These hybrid models differ from each other in hybridization configuration, solution representation and objective function. The three proposed models were abstracted into innovative methods by applying clonal selection and PSO for optimization, namely clonal selection classification algorithm (CSCA), particle swarm classification algorithm (PSCA), clonal negative selection classification algorithm (CNSCA), swarm negative selection classification algorithm (SNSCA), clonal negative selection detection algorithm (CNSDA) and swarm negative selection detection algorithm (SNSDA). These methods were evaluated on EEG data using common measures in medical diagnosis. The findings demonstrated that the methods can efficiently achieve a reliable recognition of epileptic activity in EEG signals. Although CNSCA gave the best performance, CNSDA and SNSDA are preferred due to their efficiency in time and space. A comparison with other methods in the literature showed the competitiveness of the proposed methods.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Sahel Ba-Karait, Nasser Omer
author_facet	Sahel Ba-Karait, Nasser Omer
author_sort	Sahel Ba-Karait, Nasser Omer
title	Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification
title_short	Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification
title_full	Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification
title_fullStr	Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification
title_full_unstemmed	Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification
title_sort	artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2015
url	http://eprints.utm.my/id/eprint/77766/1/NasserOmerSahelPFC2015.pdf
_version_	1747817825731346432

Artificial immune system and particle swarm optimization for electroencephalogram based epileptic seizure classification

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