The Measure of Human Vital Signals Complexity by Matrix Analysis

  • Liepa BikulčienėEmail author
  • Eurelija Venskaitytė
  • Liudas Gargasas
  • Vidmantas Jurkonis
Conference paper


The subject of investigation is extraction of information from vital signals (ECG, accelerometer, SpO2) and using it in diagnostics and assessment of status of human physiological state and complexity. The aim of this study is presentation of data recording system and the analytical methods designed for analysis of complexity and dynamic interrelations between different signal parameters. The results show that expressing of cardiac signals with Hankel and coherence matrices could be useful for diagnostic purposes.


Hankel Matrice Mealy Automaton Moore Automaton False Positive Alarm Real Time Data Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported by Agency for International Science and Technology Development Programs in Lithuania, project ITEA2 08018 GUARANTEE.


  1. 1.
    Rangayyan, S., Rangaraj, M.: Biomedical Signal Analysis - A Case-Study Approach, p. 533. Wiley, New York (2002)Google Scholar
  2. 2.
    Sliupaitė, A., Navickas, Z., Gargasas, L.: Data stream control in e-medicine using the convolution of Meally and Moore automata. In: Biomedical Engineering: Proceedings of the Conference, pp. 288–293. Technologija, Kaunas (2006)Google Scholar
  3. 3.
    Navickas, Z., Bikulčienė, L.: Expressions of solutions of ordinary differential equations by standard functions. Math. Model. Anal. 11(4), 399–412 (2006)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Kersulytė, G., Navickas, Z., Vainoras, A., Gargasas, L.: Calculation of the Hankel matrix ranks of electric and haemodynamic processes in the heart. In: Electronics and Electrical Engineering, vol. 3, issue 91, pp. 43–48. Technologija, Kaunas (2009)Google Scholar
  5. 5.
    Keršulytė, G., Navickas, Z., Raudonis, V.: Investigation of complexity of extraction accuracy modelling cardio signals in two ways. In: IDAACS’2009: Proceedings of the 5th International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, pp. 462–467, 21–23 September 2009, Rende, Italy. IEEE, Piscataway (2009)Google Scholar
  6. 6.
    Navickas, Z., Bikulčienė, L.: Informatives structures for second order matrices. In: Mathematics and Mathematical Modelling, vol. 4, pp. 26–33, Kaunas University of Technology/Kaunas. Technologija, Kaunas (2008)Google Scholar
  7. 7.
    Bikulčienė, L., Navickas, Z., Vainoras, A., Poderys, J., Ruseckas, R.: Matrix analysis of human physiologic data. In: ITI 2009: Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces, pp. 41–46, 22–25 June 2009, Cavtat/Dubrovnik, Croatia. University of Zagreb, Zagreb (2009)Google Scholar
  8. 8.
    A Guardian Angel for the Extended Home Environment.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Liepa Bikulčienė
    • 1
    • 2
    Email author
  • Eurelija Venskaitytė
    • 2
    • 3
  • Liudas Gargasas
    • 2
  • Vidmantas Jurkonis
    • 2
  1. 1.Faculty of Fundamental Sciences, Department of Applied MathematicsKaunas University of TechnologyKaunasLithuania
  2. 2.Laboratory for Automation of Cardiovascular Investigations, Institute of CardiologyLithuanian University of Health SciencesKaunasLithuania
  3. 3.Laboratory of KinesiologyLithuanian Academy of Physical EducationKaunasLithuania

Personalised recommendations