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Combination of Coarse-Grained Procedure and Fractal Dimension for Epileptic EEG Classification

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 4 (2021) · pp. 427–438

DOI
10.22146/ijccs.69845
ISSN
2460-7258, 1978-1520
Published
2021-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Epilepsy, cured by some offered treatments such as medication, surgery, and dietary plan, is a neurological brain disorder due to disturbed nerve cell activity characterized by repeated seizures. Electroencephalographic (EEG) signal processing detects and classifies these seizures as one of the abnormality types in the brain within temporal and spectral content. The proposed method in this paper employed a combination of two feature extractions, namely coarse-grained and fractal dimension, a challenge to obtain a highly accurate procedure to evaluate and predict the epileptic EEG signal of normal, interictal, and seizure classes. The result of classification accuracy using variance fractal dimension (VFD) and quadratic support machine vector (SVM) with a number scale of 10 is 99% as the highest one, excellent performance of the predictive model in terms of the error rate. In addition, a higher scale number does not determine a higher accuracy in this study.

Keywords

Collaboration network of the authors

9 authors · 15 co-authorship links · 7 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Telkom University
  • Universitas Gadjah Mada
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Vera SuryaniUniversitas Gadjah Mada1
Jondri JondriTelkom University1
Sugondo HadiyosoTelkom University1
Risanuri HidayatUniversitas Gadjah Mada1
Hanung Adi NugrohoUniversitas Gadjah Mada1
Willy Anugrah CahyadiTelkom University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Combination of Coarse-Grained Procedure and Fractal Dimension for Epileptic EEG Classification</dc:title>
	<dc:creator>Rahmawati, Dien</dc:creator>
	<dc:creator>Rizal, Achmad</dc:creator>
	<dc:creator>Silalahi, Desri Kristina</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science; Machine Learning</dc:subject>
	<dc:subject xml:lang="en-US">epilepsy; EEG classification; coarse-grained; fractal dimension; support vector machine</dc:subject>
	<dc:description xml:lang="en-US">  Epilepsy, cured by some offered treatments such as medication, surgery, and dietary plan, is a neurological brain disorder due to disturbed nerve cell activity characterized by repeated seizures. Electroencephalographic (EEG) signal processing detects and classifies these seizures as one of the abnormality types in the brain within temporal and spectral content. The proposed method in this paper employed a combination of two feature extractions, namely coarse-grained and fractal dimension, a challenge to obtain a highly accurate procedure to evaluate and predict the epileptic EEG signal of normal, interictal, and seizure classes. The result of classification accuracy using variance fractal dimension (VFD) and quadratic support machine vector (SVM) with a number scale of 10 is 99% as the highest one, excellent performance of the predictive model in terms of the error rate. In addition, a higher scale number does not determine a higher accuracy in this study.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/69845</dc:identifier>
	<dc:identifier>10.22146/ijccs.69845</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 4 (2021): October; 427-438</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/69845/32412</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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