Evolutionary coherence on EEG signals for epileptic seizure detection

Electroencephalogram (EEG) signal for epileptic seizure is nonstationary by nature. The onset of epileptic seizure is determined by the increase in synchronicity of firing neurons, and the spreading of epileptic seizure could be traced with investigating on the evolution of synchronicity across chan...

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Main Author:	Tay, Yu Cheng
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QH Natural history
Online Access:	http://eprints.utm.my/id/eprint/79558/1/TayYuChengMFBME2017.pdf
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spelling	my-utm-ep.795582018-10-31T12:58:33Z Evolutionary coherence on EEG signals for epileptic seizure detection 2017 Tay, Yu Cheng QH Natural history Electroencephalogram (EEG) signal for epileptic seizure is nonstationary by nature. The onset of epileptic seizure is determined by the increase in synchronicity of firing neurons, and the spreading of epileptic seizure could be traced with investigating on the evolution of synchronicity across channels. However, there are only a few previous studies on utilizing evolutionary coherence in detecting epileptic seizure EEG events. Besides that, these researches also mostly focus on only a few channels for mere simple and quick comparison. There is also a lack of research in comparing coherence analysis from different non-parametric approaches. Therefore, this research aims to analyze the brain connectivity in EEG epileptic seizure using nonstationary coherence by applying specifically SLEX coherence, wavelet coherence and STFT coherence. The algorithm is tested on a real epileptic seizure patient with focal epilepsy seizure at the left temporal lobe. The coherence obtained is further plotted using Circos software package, which is advantageous in mapping complex links and relationships. In conclusion, evolutionary coherence on EEG signals for epileptic seizure detection has been performed using STFT, wavelet and SLEX coherence. It was found that wavelet and SLEX coherence are capable of epileptogenic focus localization and seizure prediction, with wavelet coherence showing slightly better performance. 2017 Thesis http://eprints.utm.my/id/eprint/79558/ http://eprints.utm.my/id/eprint/79558/1/TayYuChengMFBME2017.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering Faculty of Biosciences and Medical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QH Natural history
spellingShingle	QH Natural history Tay, Yu Cheng Evolutionary coherence on EEG signals for epileptic seizure detection
description	Electroencephalogram (EEG) signal for epileptic seizure is nonstationary by nature. The onset of epileptic seizure is determined by the increase in synchronicity of firing neurons, and the spreading of epileptic seizure could be traced with investigating on the evolution of synchronicity across channels. However, there are only a few previous studies on utilizing evolutionary coherence in detecting epileptic seizure EEG events. Besides that, these researches also mostly focus on only a few channels for mere simple and quick comparison. There is also a lack of research in comparing coherence analysis from different non-parametric approaches. Therefore, this research aims to analyze the brain connectivity in EEG epileptic seizure using nonstationary coherence by applying specifically SLEX coherence, wavelet coherence and STFT coherence. The algorithm is tested on a real epileptic seizure patient with focal epilepsy seizure at the left temporal lobe. The coherence obtained is further plotted using Circos software package, which is advantageous in mapping complex links and relationships. In conclusion, evolutionary coherence on EEG signals for epileptic seizure detection has been performed using STFT, wavelet and SLEX coherence. It was found that wavelet and SLEX coherence are capable of epileptogenic focus localization and seizure prediction, with wavelet coherence showing slightly better performance.
format	Thesis
qualification_level	Master's degree
author	Tay, Yu Cheng
author_facet	Tay, Yu Cheng
author_sort	Tay, Yu Cheng
title	Evolutionary coherence on EEG signals for epileptic seizure detection
title_short	Evolutionary coherence on EEG signals for epileptic seizure detection
title_full	Evolutionary coherence on EEG signals for epileptic seizure detection
title_fullStr	Evolutionary coherence on EEG signals for epileptic seizure detection
title_full_unstemmed	Evolutionary coherence on EEG signals for epileptic seizure detection
title_sort	evolutionary coherence on eeg signals for epileptic seizure detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering
granting_department	Faculty of Biosciences and Medical Engineering
publishDate	2017
url	http://eprints.utm.my/id/eprint/79558/1/TayYuChengMFBME2017.pdf
_version_	1747818255729295360

Evolutionary coherence on EEG signals for epileptic seizure detection

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