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Cardiovascular Disease Diagnosis Method by Emerging Patterns

  • Heon Gyu Lee
  • Kiyong Noh
  • Bum Ju Lee
  • Ho-Sun Shon
  • Keun Ho Ryu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

Currently, many researches have been pursued for cardiovascular disease diagnosis using ECG so far. In this paper we extract multi-parametric features by HRV analysis from ECG, data preprocessing and heart disease pattern classification method. This study analyzes the clinical information as well as the time and the frequency domains of HRV, and then discovers cardiovascular disease patterns of patient groups. In each group, its patterns are a large frequency in one class, patients with coronary artery disease but are never found in the control or normal group. These patterns are called emerging patterns. We also use efficient algorithms to derive the patterns using the cohesion measure. Our studies show that the discovered patterns from 670 participants are used to classify new instances with higher accuracy than other reported methods

Keywords

Heart Rate Variability Association Rule Heart Rate Variability Analysis Cohesion Measure High Frequency Power 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Heon Gyu Lee
    • 1
  • Kiyong Noh
    • 2
  • Bum Ju Lee
    • 1
  • Ho-Sun Shon
    • 1
  • Keun Ho Ryu
    • 1
  1. 1.Database/Bioinformatics LaboratoryChungbuk National UniversityCheongjuKorea
  2. 2.Korea Research Institutes of Standards and ScienceKorea

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