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Support Vector Method with Radial Basis Function and Multi-Segment of Electrocardiogram for Paroxysmal Atrial Fibrillation Recognition

INDONESIAN JOURNAL OF APPLIED PHYSICS · Vol. 10 No. 2 (2020) · hlm. 148–162

DOI
10.13057/ijap.v10i2.46263
ISSN
2477-6416, 2089-0133
Terbit
2020-10-31
Penerbit
Department of Physics, Sebelas Maret University
Bahasa
eng

Abstrak

Paroxysmal Atrial fibrillation (PAF) is a heart problem relating to irregular and rapid beating of the heart atria. It has risk of stroke and is independently associated with risk of mortality. Early information of PAF episode is important for a patient to have appropriate treatment to reduce atrial fibrillation complications. This article presents a new strategy to detect PAF with base of statistical electrocardiographic features and a support vector machine (SVM). R-peak series of electrocardiogram were segmented and were extracted to find the statistics of RR intervals. Different approaches in relation with the segmentation were investigated. Two-class SVM with radial basis function (RBF) and the statistics of RR intervals were examined for PAF detection. Using clinical data of patients with PAF, the proposed strategy showed excellent performance of 99.17% in terms of accuracy. The experimental result indicated that the appropriate statistics of RR intervals and SVM-RBF with its suitable parameters can perform well for PAF detection.

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Sumber metadata: OAI-PMH (metadata jurnal) https://jurnal.uns.ac.id/ijap/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Support Vector Method with Radial Basis Function and Multi-Segment of Electrocardiogram for Paroxysmal Atrial Fibrillation Recognition</dc:title>
	<dc:creator>Nuryani, Nuryani</dc:creator>
	<dc:subject xml:lang="en-US">heart problem; irregular beat; atrial fibrillation; support vector machine; statistics; multi-segment</dc:subject>
	<dc:description xml:lang="en-US">Paroxysmal Atrial fibrillation (PAF) is a heart problem relating to irregular and rapid beating of the heart atria. It has risk of stroke and is independently associated with risk of mortality. Early information of PAF episode is important for a patient to have appropriate treatment to reduce atrial fibrillation complications. This article presents a new strategy to detect PAF with base of statistical electrocardiographic features and a support vector machine (SVM). R-peak series of electrocardiogram were segmented and were extracted to find the statistics of RR intervals. Different approaches in relation with the segmentation were investigated. Two-class SVM with radial basis function (RBF) and the statistics of RR intervals were examined for PAF detection. Using clinical data of patients with PAF, the proposed strategy showed excellent performance of 99.17% in terms of accuracy. The experimental result indicated that the appropriate statistics of RR intervals and SVM-RBF with its suitable parameters can perform well for PAF detection.</dc:description>
	<dc:publisher xml:lang="en-US">Department of Physics, Sebelas Maret University</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jurnal.uns.ac.id/ijap/article/view/46263</dc:identifier>
	<dc:identifier>10.13057/ijap.v10i2.46263</dc:identifier>
	<dc:source xml:lang="en-US">INDONESIAN JOURNAL OF APPLIED PHYSICS; Vol 10, No 2 (2020): October; 148-162</dc:source>
	<dc:source>2477-6416</dc:source>
	<dc:source>2089-0133</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jurnal.uns.ac.id/ijap/article/view/46263/29058</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 INDONESIAN JOURNAL OF APPLIED PHYSICS</dc:rights>
</oai_dc:dc>