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http://dx.doi.org/10.3745/KIPSTD.2009.16-D.1.11

Multi-parametric Diagnosis Indexes and Emerging Pattern based Classification Technique for Diagnosing Cardiovascular Disease  

Lee, Heon-Gyu (한국전자통신연구원 우정기술센터)
Noh, Ki-Yong (한국표준과학연구원)
Ryu, Keun-Ho (충북대학교 전기전자컴퓨터공학부)
Jung, Doo-Young (충북대학교 전기전자컴퓨터공학부)
Publication Information
The KIPS Transactions:PartD / v.16D, no.1, 2009 , pp. 11-26 More about this Journal
Abstract
In order to diagnose cardiovascular disease, we proposed EP-based(emerging pattern- based) classification technique using multi-parametric diagnosis indexes. We analyzed linear/nonlinear features of HRV for three recumbent postures and extracted four diagnosis indexes from ST-segments to apply the multi-parametric diagnosis indexes. In this paper, classification model using essential emerging patterns for diagnosing disease was applied. This classification technique discovers disease patterns of patient group and these emerging patterns are frequent in patients with cardiovascular disease but are not frequent in the normal group. To evaluate proposed classification algorithm, 120 patients with AP (angina pectrois), 13 patients with ACS(acute coronary syndrome) and 128 normal people data were used. As a result of classification, when multi-parametric indexes were used, the percent accuracy in classifying three groups was turned out to be about 88.3%.
Keywords
Cardiovascular Disease; Emerging Pattern Mining; Classification; Heart Rate Variability; ST-segments;
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