A cost-effective method for epileptic seizure classification

Mursalin, M; Islam, SMS; Al-Jumaily, A

A cost-effective method for epileptic seizure classification

Mursalin, M Islam, SMS Al-Jumaily, A

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019, 2020
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Mursalin, M
dc.contributor.author	Islam, SMS
dc.contributor.author	Al-Jumaily, A https://orcid.org/0000-0003-0297-2463
dc.date	2019-09-20
dc.date.accessioned	2020-09-28T04:38:44Z
dc.date.available	2020-09-28T04:38:44Z
dc.date.issued	2020
dc.identifier.citation	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019, 2020
dc.identifier.isbn	9781728117072
dc.identifier.uri	http://hdl.handle.net/10453/142894
dc.description.abstract	© 2019 IEEE. The ongoing development of various lightweight and portable EEG signal acquisition devices provides the opportunity to implement home-based epilepsy monitoring. However, it is essential to apply a highly effective method to handle the limited computational power of such devices. In this paper, we propose a cost-effective method to classify epileptic seizure using stratified sampling technique. Additionally, to reduce the required computational power, this paper proposes a novel correlation and threshold-based feature selection algorithm. For evaluating the performance of our proposed method, five different classification algorithms are applied to classify the epileptic seizure from the reduced feature set. In our experiment, the random forest classifier shows the highest accuracy compared to other classifiers.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
dc.relation.ispartof	IEEE International Conference on Signal Processing, Communications and Computing
dc.relation.isbasedon	10.1109/ICSPCC46631.2019.8960787
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A cost-effective method for epileptic seizure classification
dc.type	Conference Proceeding
utslib.location.activity	Dalian, China
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-09-28T04:38:40Z
pubs.finish-date	2019-09-22
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-09-20
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. The ongoing development of various lightweight and portable EEG signal acquisition devices provides the opportunity to implement home-based epilepsy monitoring. However, it is essential to apply a highly effective method to handle the limited computational power of such devices. In this paper, we propose a cost-effective method to classify epileptic seizure using stratified sampling technique. Additionally, to reduce the required computational power, this paper proposes a novel correlation and threshold-based feature selection algorithm. For evaluating the performance of our proposed method, five different classification algorithms are applied to classify the epileptic seizure from the reduced feature set. In our experiment, the random forest classifier shows the highest accuracy compared to other classifiers.

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http://hdl.handle.net/10453/142894