6 November 2008 Semisupervised classifier of signal-average ECG based on k-means clustering
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Proceedings Volume 7124, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008; 71240Q (2008) https://doi.org/10.1117/12.817955
Event: Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2008, 2008, Wilga, Poland
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%.
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Jacek Wydrzynski, Jacek Wydrzynski, Stanislaw Jankowski, Stanislaw Jankowski, Ewa Piatkowska-Janko, Ewa Piatkowska-Janko, } "Semisupervised classifier of signal-average ECG based on k-means clustering", Proc. SPIE 7124, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2008, 71240Q (6 November 2008); doi: 10.1117/12.817955; https://doi.org/10.1117/12.817955
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