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Entropy-Based Data Mining on the Example of Cardiac Arrhythmia Suppression

  • Martin Bachler
  • Matthias Hörtenhuber
  • Christopher Mayer
  • Andreas Holzinger
  • Siegfried Wassertheurer
Conference paper
  • 1.4k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8609)

Abstract

Heart rate variability (HRV) is the variation of the time interval between consecutive heartbeats and depends on the extrinsic regulation of the heart rate. It can be quantified using nonlinear methods such as entropy measures, which determine the irregularity of the time intervals.

In this work, approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn) and fuzzy measure entropy (FuzzyMEn) were used to assess the effects of three different cardiac arrhythmia suppressing drugs on the HRV after a myocardial infarction.

The results show that the ability of all four entropy measures to distinguish between pre- and post-treatment HRV data is highly significant (p < 0.01). Furthermore, approximate entropy and sample entropy are able to differentiate significantly (p < 0.05) between the tested arrhythmia suppressing agents.

Keywords

Data Mining Entropy Heart Rate Variability Cardiac Arrhythmia Suppression 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Martin Bachler
    • 1
    • 2
  • Matthias Hörtenhuber
    • 2
  • Christopher Mayer
    • 1
  • Andreas Holzinger
    • 3
  • Siegfried Wassertheurer
    • 1
  1. 1.Health & Environment Department, Biomedical SystemsAIT Austrian Institute of TechnologyViennaAustria
  2. 2.Institute for Analysis and Scientific ComputingVienna University of TechnologyViennaAustria
  3. 3.Research Unit HCI, Institute of Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria

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