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

Andreas Holzinger, Christof Stocker, Andreas Auinger, Manuel Bruschi, Hugo Silva, Hugo Gamboa, Ana Fred

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

19 Citations (Scopus)


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
Original languageEnglish
Title of host publicationActive Media Technology - 8th International Conference, AMT 2012, Proceedings
Number of pages12
ISBN (Print)978-3-642-35235-5
Publication statusPublished - 2012
EventInternational Conference on Active Media Technology - Macau, China
Duration: 4 Dec 20127 Dec 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7669 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Active Media Technology
Internet address


  • ApEn
  • ECG complexity
  • Information entropy
  • big data
  • knowledge discovery


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