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EARLY DETECTION AND MONITORING SYSTEM OF HEART DISEASE BASED ON ELECTROCARDIOGRAM SIGNAL

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 7 No. 1 (2014) · pp. 1–10

DOI
10.21609/jiki.v7i1.249
ISSN
2502-9274, 2088-7051
Published
2014-08-21
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Abstract Heart disease is the number one deadly disease in Indonesia. One of the main causes of fatality is the late detection of the disease. To avoid escalation of mortality caused by heart disease, we need early detection and monitoring system of heart disease. Therefore, in this research we propose an early detection and monitoring system of heart disease based on ECG signal. The proposed system has three main components: ECG hardware, smartphone, and server. Since the proposed system is designed to classify heartbeat signal, heart disease symptom can be detected as early as possible. We use FLVQ-PSO algorithm to classify heartbeat signal. Experiment result shows that classification accuracy of the system can reach 91.63%. Moreover, the proposed system can be used to verify patients heartbeat by cardiologists from distant area (telehealth). Experiment result shows that responsiveness of the system for the telehealth system is less than 0.6 seconds.

Keywords

Collaboration network of the authors

13 authors · 25 co-authorship links · 18 across different organisations

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

  • University of Indonesia
  • Institut Teknologi Sepuluh Nopember
  • Universitas Negeri Surabaya
  • Binus University
  • University of Edinburgh
  • Diponegoro University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Diane FitriaUniversity of Indonesia3
Mohammad Isa IrawanInstitut Teknologi Sepuluh Nopember2
Muhammad Febrian RachmadiUniversity of Edinburgh2
Tjan BasaruddinUniversity of Indonesia1
Adi WibowoDiponegoro University1
Muhammad Syaeful FajarUniversity of Indonesia1
Fadhlurrahman AfifUniversity of Indonesia1
Muhammad AthoillahInstitut Teknologi Sepuluh Nopember1
Umi MahdiyahInstitut Teknologi Sepuluh Nopember1
Zumrotun NafisahUniversitas Negeri Surabaya1

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://jiki.cs.ui.ac.id/index.php/jiki/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">EARLY DETECTION AND MONITORING SYSTEM OF HEART DISEASE BASED ON ELECTROCARDIOGRAM SIGNAL</dc:title>
	<dc:creator>Ma'sum, Muhammad Anwar</dc:creator>
	<dc:creator>Imah, Elly Matul</dc:creator>
	<dc:creator>Gunawan, Alexander Agung</dc:creator>
	<dc:subject xml:lang="en-US">hearth disease</dc:subject>
	<dc:subject xml:lang="en-US">early detection</dc:subject>
	<dc:subject xml:lang="en-US">monitoring</dc:subject>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">FLVQ-PSO</dc:subject>
	<dc:description xml:lang="en-US">Abstract

Heart disease is the number one deadly disease in Indonesia. One of the main causes of fatality is the late detection of the disease. To avoid escalation of mortality caused by heart disease, we need early detection and monitoring system of heart disease. Therefore, in this research we propose an early detection and monitoring system of heart disease based on ECG signal. The proposed system has three main components:  ECG hardware, smartphone, and server.  Since the proposed system is designed to classify heartbeat signal, heart disease symptom can be detected as early as possible. We use FLVQ-PSO algorithm to classify heartbeat signal. Experiment result shows that classification accuracy of the system can reach 91.63%. Moreover, the proposed system can be used to verify patients heartbeat by cardiologists from distant area (telehealth). Experiment result shows that responsiveness of the system for the telehealth system is less than 0.6 seconds.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2014-08-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/249</dc:identifier>
	<dc:identifier>10.21609/jiki.v7i1.249</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 7 No. 1 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 1-10</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v7i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/249/308</dc:relation>
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