kNN Classification of Epilepsy Brainwaves

Epilepsy is a disorder of the normal brain function by the existence of abnormal synchronous discharges in large groups of neurons in brain structures and it is estimated about 1% of the world’s population suffers from this disease [Tzallas et al., 2009]. It has been reported that the brainwave of...

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Main Authors:	Mahfuzah, Mustafa, Nor Anis Aneza, Lokman
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/5724/ http://umpir.ump.edu.my/id/eprint/5724/1/EE-003.pdf

id	ump-5724
recordtype	eprints
spelling	ump-57242018-04-11T01:57:48Z http://umpir.ump.edu.my/id/eprint/5724/ kNN Classification of Epilepsy Brainwaves Mahfuzah, Mustafa Nor Anis Aneza, Lokman TK Electrical engineering. Electronics Nuclear engineering Epilepsy is a disorder of the normal brain function by the existence of abnormal synchronous discharges in large groups of neurons in brain structures and it is estimated about 1% of the world’s population suffers from this disease [Tzallas et al., 2009]. It has been reported that the brainwave of Epilepsy patient mostly in sharp, spike and complex wave pattern [Tzallas et al., 2009]. In addition, Epilepsy brainwaves pattern lies in wide variety of Electroencephalogram (EEG) signals in formed of low-amplitude and polyspikes activity [Vargas et al., 2011]. Generally, this disease was examined through the brainwaves or EEG signals by clinical neurulogists. An EEG is a device to record the brainwaves in term of electrical activity from the brain. Brain patterns from wave shapes that are commonly sinusoidal and measured from peak to peak that range from 0.5 μV to 100 μV in amplitude. Moreover, the brainwaves have been categorized into four frequency bands, Beta (>13 Hz), Alpha (8-13 Hz), Theta (4-8 Hz) and Delta (0.5-4 Hz). All the frequency bands will be used to characterize the Epilepsy brainwave in terms of amplitude (voltage) and frequency [Mustafa et al., 2013]. The Epilepsy brainwaves were downloaded from http://www.vis.caltech.edu/~rodri/data.htm of Fp1 and Fp2 channels which is from rats. The brainwaves consists Epilepsy and non-Epilepsy samples. Then, the brainwaves were pre-processed to remove artefact (noise). Various methods had been introduced to detect spike-wave discharge in Epilepsy patient brainwave. Brainwave is nonstationary signal, therefore, time-frequency analysis is appropriate methods to analyse the signals[Tzallas et al., 2009, Vargas et al., 2011]. One of the most popular time-frequency analyses is ShortTime Fourier Transform (STFT). After the brainwaves were pre-processed, STFT was employed to the clean brainwaves. The STFT spectrogram was generated for four frequency bands of the samples. 2013 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5724/1/EE-003.pdf Mahfuzah, Mustafa and Nor Anis Aneza, Lokman (2013) kNN Classification of Epilepsy Brainwaves. In: Malaysian Technical Universities Conference on Engineering & Technology (MUCET 2013), 3-4 December 2013 , Kuantan, Pahang. pp. 1-2..
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mahfuzah, Mustafa Nor Anis Aneza, Lokman kNN Classification of Epilepsy Brainwaves
description	Epilepsy is a disorder of the normal brain function by the existence of abnormal synchronous discharges in large groups of neurons in brain structures and it is estimated about 1% of the world’s population suffers from this disease [Tzallas et al., 2009]. It has been reported that the brainwave of Epilepsy patient mostly in sharp, spike and complex wave pattern [Tzallas et al., 2009]. In addition, Epilepsy brainwaves pattern lies in wide variety of Electroencephalogram (EEG) signals in formed of low-amplitude and polyspikes activity [Vargas et al., 2011]. Generally, this disease was examined through the brainwaves or EEG signals by clinical neurulogists. An EEG is a device to record the brainwaves in term of electrical activity from the brain. Brain patterns from wave shapes that are commonly sinusoidal and measured from peak to peak that range from 0.5 μV to 100 μV in amplitude. Moreover, the brainwaves have been categorized into four frequency bands, Beta (>13 Hz), Alpha (8-13 Hz), Theta (4-8 Hz) and Delta (0.5-4 Hz). All the frequency bands will be used to characterize the Epilepsy brainwave in terms of amplitude (voltage) and frequency [Mustafa et al., 2013]. The Epilepsy brainwaves were downloaded from http://www.vis.caltech.edu/~rodri/data.htm of Fp1 and Fp2 channels which is from rats. The brainwaves consists Epilepsy and non-Epilepsy samples. Then, the brainwaves were pre-processed to remove artefact (noise). Various methods had been introduced to detect spike-wave discharge in Epilepsy patient brainwave. Brainwave is nonstationary signal, therefore, time-frequency analysis is appropriate methods to analyse the signals[Tzallas et al., 2009, Vargas et al., 2011]. One of the most popular time-frequency analyses is ShortTime Fourier Transform (STFT). After the brainwaves were pre-processed, STFT was employed to the clean brainwaves. The STFT spectrogram was generated for four frequency bands of the samples.
format	Conference or Workshop Item
author	Mahfuzah, Mustafa Nor Anis Aneza, Lokman
author_facet	Mahfuzah, Mustafa Nor Anis Aneza, Lokman
author_sort	Mahfuzah, Mustafa
title	kNN Classification of Epilepsy Brainwaves
title_short	kNN Classification of Epilepsy Brainwaves
title_full	kNN Classification of Epilepsy Brainwaves
title_fullStr	kNN Classification of Epilepsy Brainwaves
title_full_unstemmed	kNN Classification of Epilepsy Brainwaves
title_sort	knn classification of epilepsy brainwaves
publishDate	2013
url	http://umpir.ump.edu.my/id/eprint/5724/ http://umpir.ump.edu.my/id/eprint/5724/1/EE-003.pdf
first_indexed	2023-09-18T22:01:06Z
last_indexed	2023-09-18T22:01:06Z
_version_	1777414403799384064

kNN Classification of Epilepsy Brainwaves

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