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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
http://eprints.utm.my/id/eprint/79558/
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spelling my.utm.795582018-10-31T12:58:33Z http://eprints.utm.my/id/eprint/79558/ Evolutionary coherence on EEG signals for epileptic seizure detection 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79558/1/TayYuChengMFBME2017.pdf Tay, Yu Cheng (2017) Evolutionary coherence on EEG signals for epileptic seizure detection. Masters thesis, Universiti Teknologi Malaysia, Faculty of Biosciences and Medical Engineering.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2017
url http://eprints.utm.my/id/eprint/79558/1/TayYuChengMFBME2017.pdf
http://eprints.utm.my/id/eprint/79558/
_version_ 1643658230351527936
score 13.244367

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