Regularized universum twin support vector machine for classification of EEG signal

Gupta, D; Sarma, HJ; Mishra, K; Prasad, M

Regularized universum twin support vector machine for classification of EEG signal

Gupta, D Sarma, HJ Mishra, K Prasad, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2019, 2019-October, pp. 2298-2304
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Gupta, D
dc.contributor.author	Sarma, HJ
dc.contributor.author	Mishra, K
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date	2019-09-06
dc.date.accessioned	2023-03-24T10:39:54Z
dc.date.available	2023-03-24T10:39:54Z
dc.date.issued	2019-10-01
dc.identifier.citation	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2019, 2019-October, pp. 2298-2304
dc.identifier.isbn	9781728145693
dc.identifier.issn	1062-922X
dc.identifier.issn	2577-1655
dc.identifier.uri	http://hdl.handle.net/10453/168375
dc.description.abstract	© 2019 IEEE. Electroencephalogram signal is the signal used for the detection of a neurological disorder as epilepsy disorder, sleep disorder and many more. The types of EEG signal gives the hidden information regarding the distribution of the data that may consist of a large volume of the poor and noisy signal. In order to reduce the outlier effects and noise, incorporation of prior knowledge in the model, universum may help and enhance the better generalization ability of the model. This paper proposes a regularized universum twin support vector machine (RUTWSVM) for classification of the healthy and seizure EEG signals. Here, the selection of the universum data points is obtained in two ways (i). Universum data has been generated from the healthy and seizure EEG signals itself and (ii). Interictal EEG signal has been used as universum data which may help to handle the outlier effects. Further, various feature selection techniques are applied to extract the important noise free features from the EEG signals. We have performed a comparative analysis of proposed RUTWSVM with USVM and UTWSVM to classify the EEG signals as well as benchmark real-world datasets in an optimum way. The experiment results clearly exhibit the applicability and usability of the proposed RUTWSVM with interictal EEG signals as universum data points as well as benchmark real-world datasets.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
dc.relation.ispartof	IEEE International Conference on Systems, Man and Cybernetics
dc.relation.ispartofseries	IEEE International Conference on Systems Man and Cybernetics Conference Proceedings
dc.relation.isbasedon	10.1109/SMC.2019.8913897
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Regularized universum twin support vector machine for classification of EEG signal
dc.type	Conference Proceeding
utslib.citation.volume	2019-October
utslib.location.activity	Bari, Italy
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0903 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-03-24T10:39:52Z
pubs.finish-date	2019-09-09
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-09-06
pubs.volume	2019-October
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Electroencephalogram signal is the signal used for the detection of a neurological disorder as epilepsy disorder, sleep disorder and many more. The types of EEG signal gives the hidden information regarding the distribution of the data that may consist of a large volume of the poor and noisy signal. In order to reduce the outlier effects and noise, incorporation of prior knowledge in the model, universum may help and enhance the better generalization ability of the model. This paper proposes a regularized universum twin support vector machine (RUTWSVM) for classification of the healthy and seizure EEG signals. Here, the selection of the universum data points is obtained in two ways (i). Universum data has been generated from the healthy and seizure EEG signals itself and (ii). Interictal EEG signal has been used as universum data which may help to handle the outlier effects. Further, various feature selection techniques are applied to extract the important noise free features from the EEG signals. We have performed a comparative analysis of proposed RUTWSVM with USVM and UTWSVM to classify the EEG signals as well as benchmark real-world datasets in an optimum way. The experiment results clearly exhibit the applicability and usability of the proposed RUTWSVM with interictal EEG signals as universum data points as well as benchmark real-world datasets.

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http://hdl.handle.net/10453/168375