Advertisement

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)

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.

Keywords

Information entropy ApEn big data knowledge discovery ECG complexity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Berntson, G.G., Bigger, J.T., Eckberg, D.L., Grossman, P., Kaufmann, P.G., Malik, M., Nagaraja, H.N., Porges, S.W., Saul, J.P., Stone, P.H., van der Molen, M.W.: Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34(6), 623–648 (1997)CrossRefGoogle Scholar
  2. 2.
    Porta, A., Guzzetti, S., Montano, N., Furlan, R., Pagani, M., Malliani, A., Cerutti, S.: Entropy, entropy rate, and pattern classification as tools to typify complexity in short heart period variability series. IEEE Transactions on Biomedical Engineering 48(11), 1282–1291 (2001)CrossRefGoogle Scholar
  3. 3.
    Batchinsky, A.I., Salinas, J., Cancio, L.C.: Assessment of the need to perform life-saving interventions using comprehensive analysis of the electrocardiogram and artificial neural networks. In: RTO-MP-HFM-182: Use of Advanced Technologies and New Procedures in Medical Field Operations, vol. 39, pp. 1–16 (2010)Google Scholar
  4. 4.
    Buchman, T.G.: Nonlinear dynamics, complex systems, and the pathobiology of critical illness. Curr. Opin. Crit. Care 10(5), 378–382 (2004)CrossRefGoogle Scholar
  5. 5.
    Holzinger, A., Popova, E., Peischl, B., Ziefle, M.: On Complexity Reduction of User Interfaces for Safety-Critical Systems. In: Quirchmayr, G., Basl, J., You, I., Xu, L., Weippl, E. (eds.) CD-ARES 2012. LNCS, vol. 7465, pp. 108–122. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United States of America 88(6), 2297–2301 (1991)MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    Simonic, K., Holzinger, A., Bloice, M., Hermann, J.: Optimizing long-term treatment of rheumatoid arthritis with systematic documentation. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 550–554 (May 2011)Google Scholar
  8. 8.
    Pincus, S.M.: Approximate entropy (ApEn) as a complexity measure. Chaos 5, 110–117 (1995)CrossRefGoogle Scholar
  9. 9.
    Gamboa, H.: Multi-Modal Behavioral Biometrics Based on HCI and Electrophysiology. PhD thesis, Universidade Tecnica de Lisboa, Instituto Superior Tecnico (2008)Google Scholar
  10. 10.
    Clifford, G., Azuaje, F., McSharry, P.: Artech House engineering in medicine & biology series. In: ECG Statistics, Noise, Artifacts, and Missing Data, pp. 55–99. Artech House (2006)Google Scholar
  11. 11.
    Akselrod, S., Gordon, D., Ubel, F.A., Shannon, D.C., Berger, A.C., Cohen, R.J.: Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 213(4504), 220–222 (1981)CrossRefGoogle Scholar
  12. 12.
    Berntson, G.G., Stowell, J.R.: Ecg artifacts and heart period variability: don’t miss a beat! Psychophysiology 35(1), 127–132 (1998)CrossRefGoogle Scholar
  13. 13.
    Acharya, U.R., Molinari, F., Sree, S.V., Chattopadhyay, S., Ng, K.H., Suri, J.S.: Automated diagnosis of epileptic eeg using entropies. Biomedical Signal Processing and Control 7(4), 401–408 (2012)CrossRefGoogle Scholar
  14. 14.
    Hornero, R., Aboy, M., Abasolo, D., McNames, J., Wakeland, W., Goldstein, B.: Complex analysis of intracranial hypertension using approximate entropy. Crit. Care Med. 34(1), 87–95 (2006)CrossRefGoogle Scholar
  15. 15.
    Holzinger, A.: On knowledge discovery and interactive intelligent visualization of biomedical data - challenges in human computer interaction & biomedical informatics. In: Conference on e-Business and Telecommunications (ICETE 2012), Rome, Italy, pp. IS9–IS20 (2012)Google Scholar
  16. 16.
    Holzinger, A., Scherer, R., Seeber, M., Wagner, J., Müller-Putz, G.: Computational Sensemaking on Examples of Knowledge Discovery from Neuroscience Data: Towards Enhancing Stroke Rehabilitation. In: Böhm, C., Khuri, S., Lhotská, L., Renda, M.E. (eds.) ITBAM 2012. LNCS, vol. 7451, pp. 166–168. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Holzinger, A., Simonic, K., Yildirim, P.: Disease-disease relationships for rheumatic diseases: Web-based biomedical textmining and knowledge discovery to assist medical decision making. In: 36th International Conference on Computer Software and Applications, COMPSAC, Izmir, Turkey, pp. 573–580. IEEE (2012)Google Scholar
  18. 18.
    Takens, F.: Invariants related to dimension and entropy. In: Atas do 13, Col. brasiliero de Matematicas, Rio de Janeiro (1983)Google Scholar

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

Personalised recommendations