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SINTA 1
Feature extraction of electrocardiogram signal using machine learning classification
International Journal of Electrical and Computer Engineering (IJECE)
ISSN 2722-2578, 2088-8708
Published 2020-12-01
Publisher Institute of Advanced Engineering and Science
Abstract
In this article, we'll introduce ways to build virtual worlds through different computer programs. We will show the method of rectangles for analyzing data obtained from the electroencephalogram. We will demonstrate basic mathematical models for movement prediction in a system of virtual reality. Using this data, the main transformations are possible-change of position and rotation (change of orientation).
Collaboration network of the authors
3 authors ·
3 co-authorship links
· 2 across different organisations
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This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.
Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works,
first harvested 27 Okt 2026,
last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Feature extraction of electrocardiogram signal using machine learning classification</dc:title><dc:creator>Kuila, Sumanta</dc:creator><dc:creator>Dhanda, Namrata</dc:creator><dc:creator>Joardar, Subhankar</dc:creator><dc:description><jats:p>In this article, we'll introduce ways to build virtual worlds through different computer programs. We will show the method of rectangles for analyzing data obtained from the electroencephalogram. We will demonstrate basic mathematical models for movement prediction in a system of virtual reality. Using this data, the main transformations are possible-change of position and rotation (change of orientation).</jats:p></dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2020-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v10i6.pp6598-6605</dc:identifier><dc:identifier>10.11591/ijece.v10i6.pp6598-6605</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 10 No 6 (2020); 6598</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/21321/14447</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>
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24 citations
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Counts as reported by each index on 28 Okt 2026; indexes count citations differently.
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Cite this article
APA 7
Kuila, S., Dhanda, N., & Joardar, S. (2020). Feature extraction of electrocardiogram signal using machine learning classification. International Journal of Electrical and Computer Engineering (IJECE), 10(6). https://doi.org/10.11591/ijece.v10i6.pp6598-6605
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IEEE
S. Kuila, N. Dhanda, and S. Joardar, “Feature extraction of electrocardiogram signal using machine learning classification,” International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 6, 2020, doi: 10.11591/ijece.v10i6.pp6598-6605.
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