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

doi:10.1007/978-3-642-35236-2_64

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 proceedings › Conference contribution › peer-review

19 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Active Media Technology - 8th International Conference, AMT 2012, Proceedings
Publisher	Springer
Pages	646-657
Number of pages	12
ISBN (Print)	978-3-642-35235-5
DOIs	https://doi.org/10.1007/978-3-642-35236-2_64
Publication status	Published - 2012
Event	International Conference on Active Media Technology - Macau, China Duration: 4 Dec 2012 → 7 Dec 2012 http://www.fst.umac.mo/wic2012/AMT/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7669 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Active Media Technology
Country/Territory	China
City	Macau
Period	04.12.2012 → 07.12.2012
Internet address	http://www.fst.umac.mo/wic2012/AMT/

Keywords

ApEn
ECG complexity
Information entropy
big data
knowledge discovery

Access to Document

10.1007/978-3-642-35236-2_64

Cite this

Holzinger, A., Stocker, C., Auinger, A., Bruschi, M., Silva, H., Gamboa, H., & Fred, A. (2012). On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data. In Active Media Technology - 8th International Conference, AMT 2012, Proceedings (pp. 646-657). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7669 LNCS). Springer. https://doi.org/10.1007/978-3-642-35236-2_64

Holzinger, Andreas ; Stocker, Christof ; Auinger, Andreas et al. / On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data. Active Media Technology - 8th International Conference, AMT 2012, Proceedings. Springer, 2012. pp. 646-657 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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",

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author = "Andreas Holzinger and Christof Stocker and Andreas Auinger and Manuel Bruschi and Hugo Silva and Hugo Gamboa and Ana Fred",

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Holzinger, A, Stocker, C, Auinger, A, Bruschi, M, Silva, H, Gamboa, H & Fred, A 2012, On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data. in Active Media Technology - 8th International Conference, AMT 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7669 LNCS, Springer, pp. 646-657, International Conference on Active Media Technology, Macau, China, 04.12.2012. https://doi.org/10.1007/978-3-642-35236-2_64

On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data. / Holzinger, Andreas; Stocker, Christof; Auinger, Andreas et al.
Active Media Technology - 8th International Conference, AMT 2012, Proceedings. Springer, 2012. p. 646-657 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7669 LNCS).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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AU - Stocker, Christof

AU - Auinger, Andreas

AU - Bruschi, Manuel

AU - Silva, Hugo

AU - Gamboa, Hugo

AU - Fred, Ana

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Holzinger A, Stocker C, Auinger A, Bruschi M, Silva H, Gamboa H et al. On Applying Approximate Entropy to ECG Signals for Knowledge Discovery on the Example of Big Sensor Data. In Active Media Technology - 8th International Conference, AMT 2012, Proceedings. Springer. 2012. p. 646-657. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-35236-2_64

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

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