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Detection of Atrial Fibrillation using Autoregressive modeling

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 5 No. 1 (2015)

DOI
10.11591/ijece.v5i1.pp64-70
ISSN
2088-8708, 2088-8708
Published
2015-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

A ‎ atrial fibrillation (AF) is the arrhythmia that commonly causes death in the adults. We measured AR coefficients using Burg’s method for each 15 second segment of ECG. These features are classified using the different statistical classifiers: kernel SVM and KNN classifier. The performance of the algorithm was evaluated on signals from MIT Physionet database.. The effect of AR model order and data length was tested on the classification results. This method shows better results can be used for practical use in the clinics. ‏

Collaboration network of the authors

2 authors · 1 co-authorship link

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  • Siddhartha Academy of Higher Education
  • Affiliation unknown
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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>Detection of Atrial Fibrillation using Autoregressive modeling</dc:title><dc:creator>Padmavathi, Kora</dc:creator><dc:creator>Ramakrishna, K.Sri</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;p class="Abstract"&amp;gt;A&amp;lt;span lang="AR-SA"&amp;gt;‎&amp;lt;/span&amp;gt;atrial fibrillation (AF) is the arrhythmia that commonly causes death in the adults. We measured AR coefficients using Burg’s method for each 15 second segment of ECG. These features are classified using the different statistical classifiers: kernel SVM and KNN classifier. The performance of the algorithm was evaluated on signals from MIT Physionet database.. The effect of AR model order and data length was tested on the classification results. This method shows better results can be used for practical use in the clinics.&amp;lt;span lang="AR-SA"&amp;gt;‏&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2015-02-01</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.v5i1.pp64-70</dc:identifier><dc:identifier>10.11591/ijece.v5i1.pp64-70</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 5 No 1 (2015); 64</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/5617/4778</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>