On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data

  • Andreas Holzinger
  • Christof Stocker
  • Manuel Bruschi
  • Andreas Auinger
  • Hugo Silva
  • Hugo Gamboa
  • Ana Fred
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7669)


Information entropy as a universal and fascinating statistical concept is helpful for numerous problems in the computational sciences. Approximate entropy (ApEn), introduced by Pincus (1991), can classify complex data in diverse settings. The capability to measure complexity from a relatively small amount of data holds promise for applications of ApEn in a variety of contexts. In this work we apply ApEn to ECG data. The data was acquired through an experiment to evaluate human concentration from 26 individuals. The challenge is to gain knowledge with only small ApEn windows while avoiding modeling artifacts. Our central hypothesis is that for intra subject information (e.g. tendencies, fluctuations) the ApEn window size can be significantly smaller than for inter subject classification. For that purpose we propose the term truthfulness to complement the statistical validity of a distribution, and show how truthfulness is able to establish trust in their local properties.


Information entropy ApEn big data knowledge discovery ECG complexity 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andreas Holzinger
    • 1
  • Christof Stocker
    • 1
  • Manuel Bruschi
    • 1
  • Andreas Auinger
    • 2
  • Hugo Silva
    • 3
  • Hugo Gamboa
    • 4
  • Ana Fred
    • 3
  1. 1.Institute for Medical Informatics, Statistics & Documentation, Research Unit Human-Computer InteractionMedical University GrazGrazAustria
  2. 2.Upper Austria University of Applied SciencesSteyrAustria
  3. 3.IT - Instituto de TelecomunicaçõesTechnical University LisbonLisbonPortugal
  4. 4.Physics Department, Faculty of Sciences and TechnologyNew University of LisbonCaparicaPortugal

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