The Higher-Order Spectra as a Tool for Assessing the Progress in Rehabilitation of Patients After Ischemic Brain Stroke

  • Ewaryst Tkacz
  • Zbigniew Budzianowski
  • Wojciech Oleksy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)


This article explores the possibility of using the higher-order spectra as a tool to determine the progress in rehabilitation of patients after ischemic brain stroke (IBS). In order to evaluate the effectiveness of this tool patients’ heart rate variability (HRV) recordings obtained at 1, 10 and 60 day after stroke are listed and compared to those recorded for healthy individuals. Each set of HRV signals is processed with bispectral and bicoherent analysis. In each case three statistical parameters are observed. The values of investigated parameters differ for healthy people and these affected by the IBS and the rehabilitation progress is visible based on the parameters changing their values towards these characteristic for healthy individuals. The obtained results show usefulness of higher-order spectra as a tool for assessment of the rehabilitation progress. Authors believe that further work would greatly improve potential of the described tool, allowing to increase its precision and expand the application field.


Heart rate variability Higher-order statistics Signal processing 


  1. 1.
    PhysioNet. Accessed 5 Mar 2015
  2. 2.
    Swami, A., Mendel, J., Chrysostomos, L.: Higher-Order Spectral Analysis Toolbox: For Use with MATLAB. The MathWorks, Inc. (1993)Google Scholar
  3. 3.
    Goshvarpour, A., et al.: Comparison of higher order spectra in heart rate signals during two techniques of meditation: Chi and Kundalini meditation. Cogn. Neurodyn. 7(1), 39–46 (2013)CrossRefGoogle Scholar
  4. 4.
    Chua, C.: Analysis of cardiac and epileptic signals using higher order spectra, Praca doktorska. Queensland University of Technology, Queensland (2010)Google Scholar
  5. 5.
    Krauze, T., Guzik, P., Wysocki, H.: Zmienność rytmu serca: aspekty techniczne. Nowiny Lekarskie 9(70), 973–984 (2001)Google Scholar
  6. 6.
    Mazur, P., Pfitzner, R., Matusik, P.: Analiza parametrów częstotliwościowych zmienności rytmu serca po pomostowaniu aortalno-wieńcowym. Folia Cardiologica 6(1), 76–81 (2011)Google Scholar
  7. 7.
    Kłopocka, M., Budzyński, J., Bujak, R., Świątkowski, M., Sinkiewicz, W., Ziółkowski, M.: Dobowa zmienność rytmu zatokowego serca jako wskaźnik aktywności autonomicznego układu nerwowego u mężczyzn z zespołem zależności alkoholowej w okresie abstynencji. Alkoholizm i Narkomania 13(4), 491–501 (2000)Google Scholar
  8. 8.
    Jouny, I., Moses, R.: The bispectrum of complex signals: definitions and properties. IEEE Trans. Signal Process. 40(11), 2833–2836 (1992)CrossRefzbMATHGoogle Scholar
  9. 9.
    Saliu, S., Birand, A., Kudaiberdieva, G.: Bispectral Analysis of Heart Rate Variability Signal (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ewaryst Tkacz
    • 1
  • Zbigniew Budzianowski
    • 1
  • Wojciech Oleksy
    • 1
  1. 1.Department of Biosensors and Biomedical Signals Processing, Faculty of Biomedical EngineeringTechnical University of SilesiaZabrzePoland

Personalised recommendations