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Proceedings Paper

Semisupervised classifier of signal-average ECG based on k-means clustering
Author(s): Jacek Wydrzynski; Stanislaw Jankowski; Ewa Piatkowska-Janko
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Paper Abstract

This paper presents the method of risk recognition of sustained ventricular tachycardia and flicker in patients after myocardial infarction based on high-resolution and signal-averaged electrocardiography. Described semisupervised method is combination of k-means clustering and support vector machine classifier. The work is based on dataset obtained from the Medical University of Warsaw. While learning process there were used only 5% examples labels. Evolutionary optimization of coefficients for each signal parameter was executed. It let show the most important parameters. The method of classification had high rate of successful recognition about 94.9%.

Paper Details

Date Published: 6 November 2008
PDF: 6 pages
Proc. SPIE 7124, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008, 71240Q (6 November 2008); doi: 10.1117/12.817955
Show Author Affiliations
Jacek Wydrzynski, Warsaw Univ. of Technology (Poland)
Stanislaw Jankowski, Warsaw Univ. of Technology (Poland)
Ewa Piatkowska-Janko, Warsaw Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 7124:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008
Ryszard S. Romaniuk; Tomasz R. Wolinski, Editor(s)

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