Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms

Epileptic seizures are indicators of epilepsy. Thorough analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diag...

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Main Authors:	Qayoom, Abdul, Abdul Rahman, Abdul Wahab, Kamaruddin, Norhaslinda
Format:	Conference or Workshop Item
Language:	English English
Published:	International Society of Computers and Their Applications (ISCA) 2014
Subjects:	QP Physiology TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/58416/ http://irep.iium.edu.my/58416/ http://irep.iium.edu.my/58416/1/58416_Analysis%20of%20EEG%20signals_complete.pdf http://irep.iium.edu.my/58416/2/58416_Analysis%20of%20EEG%20signals_scopus.pdf

id	iium-58416
recordtype	eprints
spelling	iium-584162019-01-24T06:18:12Z http://irep.iium.edu.my/58416/ Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms Qayoom, Abdul Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda QP Physiology TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Epileptic seizures are indicators of epilepsy. Thorough analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper reviews the fundamental operations of Mathematical Morphology and its application in EEG signals processing. The nature of epileptic EEG is hidden in its geometric structure and Mathematical Morphology is applied to decompose and quantize EEG Signal based on its geometric structure. Kurtosis which gives measure of peakiness of a signal is calculated for each of the constituents from which the feature vector is constructed. Multi-layer Perceptron (MLP) is used for classification to differentiate between various types of EEG classes. The differentiation between epileptic and normal EEG is achieved with accuracy of around 90%. International Society of Computers and Their Applications (ISCA) 2014-10 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/58416/1/58416_Analysis%20of%20EEG%20signals_complete.pdf application/pdf en http://irep.iium.edu.my/58416/2/58416_Analysis%20of%20EEG%20signals_scopus.pdf Qayoom, Abdul and Abdul Rahman, Abdul Wahab and Kamaruddin, Norhaslinda (2014) Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms. In: 27th International Conference on Computer Applications in Industry and Engineering, CAINE 2014, 13-15 October 2014, New Orleans; United States. http://toc.proceedings.com/24275webtoc.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QP Physiology TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QP Physiology TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Qayoom, Abdul Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms
description	Epileptic seizures are indicators of epilepsy. Thorough analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper reviews the fundamental operations of Mathematical Morphology and its application in EEG signals processing. The nature of epileptic EEG is hidden in its geometric structure and Mathematical Morphology is applied to decompose and quantize EEG Signal based on its geometric structure. Kurtosis which gives measure of peakiness of a signal is calculated for each of the constituents from which the feature vector is constructed. Multi-layer Perceptron (MLP) is used for classification to differentiate between various types of EEG classes. The differentiation between epileptic and normal EEG is achieved with accuracy of around 90%.
format	Conference or Workshop Item
author	Qayoom, Abdul Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda
author_facet	Qayoom, Abdul Abdul Rahman, Abdul Wahab Kamaruddin, Norhaslinda
author_sort	Qayoom, Abdul
title	Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms
title_short	Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms
title_full	Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms
title_fullStr	Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms
title_full_unstemmed	Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms
title_sort	analysis of eeg signals using mathematical morphology decomposition and kurtosis: detection of epileptiforms
publisher	International Society of Computers and Their Applications (ISCA)
publishDate	2014
url	http://irep.iium.edu.my/58416/ http://irep.iium.edu.my/58416/ http://irep.iium.edu.my/58416/1/58416_Analysis%20of%20EEG%20signals_complete.pdf http://irep.iium.edu.my/58416/2/58416_Analysis%20of%20EEG%20signals_scopus.pdf
first_indexed	2023-09-18T21:22:36Z
last_indexed	2023-09-18T21:22:36Z
_version_	1777411981772324864

Analysis of EEG signals using mathematical morphology decomposition and kurtosis: Detection of epileptiforms

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