Semisupervised classifier of signal-average ECG based on k-means clustering

被引：0
|
作者：
Wydrzynski, Jacek [1 ]
Jankowski, Stanislaw [1 ]
Piatkowska-Janko, Ewa [2 ]
机构：
[1] Warsaw Univ Technol, Inst Elect Syst, Nowowiejska 15-19, PL-00665 Warsaw, Poland
[2] Warsaw Univ Technol, Inst Radioelect, Warsaw, Poland
来源：
PHOTONICS APPLICATIONS IN ASTRONOMY, COMMUNICATIONS, INDUSTRY, AND HIGH-ENERGY PHYSICS EXPERIMENTS 2008 | 2008年 / 7124卷
关键词：
semisupervised learning; support vector machine; k-means clustering; high-resolution electrocardiography; sustained ventricular tachycardia;
D O I：
10.1117/12.817955
中图分类号：
P1 [天文学];
学科分类号：
0704 ;
摘要：
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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页数：6
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