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Universal Multi-complexity Measures for Physiological State Quantification in Intelligent Diagnostics and Monitoring Systems

  • Olga Senyukova
  • Valeriy Gavrishchaka
  • Mark Koepke
Part of the Communications in Computer and Information Science book series (CCIS, volume 404)

Abstract

Previously we demonstrated that performance of heart rate variability indicators computed from necessarily short time series could be significantly improved by combination of complexity measures using boosting algorithms. Here we argue that these meta-indicators could be further incorporated into various intelligent systems. They can be combined with other statistical techniques without additional recalibration. For example, usage of distribution moments of these measures computed on consecutive short segments of the longer time series could increase diagnostics accuracy and detection rate of emerging abnormalities. Multiple physiological regimes are implicitly encoded in such ensemble of base indicators. Using an ensemble as a state vector and defining distance metrics between these vectors, the encoded fine-grain knowledge can be utilized using instance-based learning, clustering algorithms, and graph-based techniques. We conclude that the length change of minimum spanning tree based on these metrics provides an early indication of developing abnormalities.

Keywords

ensemble learning boosting complexity measures physiological indicators heart rate variability graph-based clustering 

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References

  1. 1.
    Seely, A.J.-E., Macklem, P.T.: Complex systems and the technology of variability analysis. Critical Care 8, 367–384 (2004)CrossRefGoogle Scholar
  2. 2.
    Belair, J., et al.: Dynamical Disease: Mathematical Analysis of Human Illness. AIP Press, New York (1995)zbMATHGoogle Scholar
  3. 3.
    Voss, A., Schulz, S., Schroederet, R., et al.: Methods derived from nonlinear dynamics for analyzing heart rate variability. Phylosophical Transactions of the Royal Society A 367, 277–296 (2008)CrossRefGoogle Scholar
  4. 4.
    Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology: Heart Rate Variability: Standards of Measurement, Physiological Interpretation and Clinical Use. Circulation 93, 1043–1065 (1996)Google Scholar
  5. 5.
    Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997)zbMATHGoogle Scholar
  6. 6.
    Gavrishchaka, V.V., Senyukova, O.: Robust algorithmic detection of the developed cardiac pathologies and emerging or transient abnormalities from short periods of RR data. In: 2011 International Symposium on Computational Models for Life Sciences, AIP Conference Proceedings, New York, pp. 215–224 (2011)Google Scholar
  7. 7.
    Gavrishchaka, V.V., Senyukova, O.V.: Robust Algorithmic Detection of Cardiac Pathologies from Short Periods of RR Data. In: Pham, T., Jain, L.C. (eds.) Knowledge-Based Systems in Biomedicine. SCI, vol. 450, pp. 137–153. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Senyukova, O., Gavrishchaka, V.: Ensemble Decomposition Learning for Optimal Utilization of Implicitly Encoded Knowledge in Biomedical Applications. In: IASTED International Conference on Computational Intelligence and Bioinformatics, pp. 69–73. ACTA Press, Calgary (2011)Google Scholar
  9. 9.
    Bart, E., Ullman, S.: Single-example learning of novel classes using representation by similarity. In: British Machine Vision Conference (2005)Google Scholar
  10. 10.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and Techniques. Morgan Kaufmann, San Francisco (2005)Google Scholar
  11. 11.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, London (1999)Google Scholar
  12. 12.
    Peng, C.-K., Havlin, S., Stanley, E.H., Goldberger, A.L.: Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5, 82–87 (1995)CrossRefGoogle Scholar
  13. 13.
    Costa, M., Goldberger, A.L., Peng, C.-K.: Multiscale entropy analysis of biological signals. Physical Review Letters E 71, 021906 (2005)Google Scholar
  14. 14.
    Makowiec, D., Dudkowska, A., Zwierz, M., et al.: Scale Invariant Properties in Heart Rate Signals 37, Acta Physica Polonica B, Cracow (2006)Google Scholar
  15. 15.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge (1992)Google Scholar
  16. 16.
    Schapire, R.E.: The Design and Analysis of Efficient Learning Algorithms. MIT Press, Cambridge (1992)Google Scholar
  17. 17.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  18. 18.
    Jin, R., Liu, Y., Si, L., et al.: A new boosting algorithm using input-dependent regularizer. In: 20th International Conference on Machine Learning. AAAI Press, Palo Alto (2003)Google Scholar
  19. 19.
    Gavrishchaka, V.V., Senyukova, O.V., Koepke, M.E., Kryuchkova, A.I.: Multi-objective physiological indicators based on complementary complexity measures: application to early diagnostics and prediction of acute events. In: International Conference on Computer and Computational Intelligence, ASME Digital Collection, pp. 95–106 (2011)Google Scholar
  20. 20.
    Onnela, J.-P., Chakraborti, A., Kaski, K., et al.: Dynamics of market correlations: Taxonomy and portfolio analysis. Physical Review E 68, 056110 (2003)Google Scholar
  21. 21.
    Tumminello, M., Lillo, F., Mantegna, R.N.: Correlation, hierarchies, and networks in financial markets. Journal of Economic Behavior & Organization 75, 40–58 (2010)CrossRefGoogle Scholar
  22. 22.
    Mantegna, R.N.: Hierarchical structure in financial markets. European Physics Journal B 11, 193–197 (1999)CrossRefGoogle Scholar
  23. 23.
    Vandewalle, N., Brisbois, F., Tordoir, X.: Non-random topology of stock markets. Quantitative Finance 1, 372–374 (2001)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Olga Senyukova
    • 1
  • Valeriy Gavrishchaka
    • 2
  • Mark Koepke
    • 2
  1. 1.Department of Computational Mathematics and CyberneticsLomonosov Moscow State UniversityLeninskie GoryRussia
  2. 2.Department of PhysicsWest Virginia UniversityMorgantownUSA

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