Signal, Image and Video Processing

, Volume 13, Issue 8, pp 1521–1528 | Cite as

Detecting changes in cardiovascular interaction during postural stress using directed coherence

  • M. C. Helen MaryEmail author
  • Dilbag Singh
  • K. K. Deepak
Original Paper


In the study, Granger causality frequency analysis based on directed coherence is utilized to detect and quantify directional interaction among heart rate, systolic blood pressure and respiratory signal during postural stress from supine to standing and from standing to supine. The directional information flow during postural stress from supine to standing is dominant on cardiorespiratory interaction, vasculo-respiratory interaction and cardiovascular interaction. Conversely, the reverse postural stress from standing to supine resulted in a contrary effect on both cardiorespiratory and cardiovascular interactions without significant change in vasculo-respiratory interaction. Based on the power change, we observed that postural stress gets affected by both cardiorespiratory and cardiovascular but not by vasculo-respiratory system. Therefore, the directed coherence power calculated for each interaction helps to identify short-term regulatory mechanism during postural stress.


Frequency analysis Directed coherence (DC) Cardiovascular Respiration Postural stress 



Arterial blood pressure




Directed coherence




High frequency


Low frequency


Multivariate autoregressive




Power spectral density

List of symbols


Model coefficient matrix

\(\varGamma \)


\(\gamma \)

Directed coherence power



The authors would like to thank Biomedical Instrumentation Laboratory, Department of Instrumentation and Control, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, and to all volunteers who took part in the recording.


  1. 1.
    Acharya, U.R., Joseph, K.P., Kannathal, N., Lim, C.M., Suri, J.S.: Heart Rate Variability, Advances in Cardiac Signal Processing, pp. 121–165. Springer, Berlin (2007)CrossRefGoogle Scholar
  2. 2.
    Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Akselrod, S., Gordon, D., Ubel, F.A., Shannon, D.C., Berger, A., 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
  4. 4.
    Baccala, L., Sameshima, K., Ballester, G., Do Valle, A., Timo-Iaria, C.: Studying the interaction between brain structures via directed coherence and Granger causality. Appl. Signal Process. 5(1), 40 (1998)CrossRefGoogle Scholar
  5. 5.
    Baselli, G., Cerutti, S., Livraghi, M., Meneghini, C., Pagani, M., Rimoldi, O.: Causal relationship between heart rate and arterial blood pressure variability signals. Med. Biol. Eng. Comput. 26(4), 374–378 (1988)CrossRefGoogle Scholar
  6. 6.
    Baselli, G., Porta, A., Rimoldi, O., Pagani, M., Cerutti, S.: Spectral decomposition in multichannel recordings based on multivariate parametric identification. IEEE Trans. Biomed. Eng. 44(11), 1092–1101 (1997)CrossRefGoogle Scholar
  7. 7.
    De Boer, R., Karemaker, J., Strackee, J.: Relationships between short-term blood-pressure fluctuations and heart-rate variability in resting subjects I: a spectral analysis approach. Med. Biol. Eng. Comput. 23(4), 352–358 (1985)CrossRefGoogle Scholar
  8. 8.
    Eftaxias, K., Sanei, S.: Discrimination of task-related eeg signals using diffusion adaptation and s-transform coherency. In: IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1-6. (2014)Google Scholar
  9. 9.
    Eichler, M.: Causal inference with multiple time series: principles and problems. Philos. Trans. R. Soc. A 371(1997), 20110613 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Escudero, J., Sanei, S., Jarchi, D., Abasolo, D., Hornero, R.: Regional coherence evaluation in mild cognitive impairment and alzheimer’s disease based on adaptively extracted magnetoencephalogram rhythms. Physiol. Meas. 32(8), 1163 (2011)CrossRefGoogle Scholar
  11. 11.
    Faes, L., Erla, S., Porta, A., Nollo, G.: A framework for assessing frequency domain causality in physiological time series with instantaneous effects. Philos. Trans. R. Soc. A 371(1997), 20110618 (2013)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Faes, L., Nollo, G.: Multivariate frequency domain analysis of causal interactions in physiological time series. In: Biomedical Engineering, Trends in Electronics, Communications and Software. InTech (2011)Google Scholar
  13. 13.
    Faes, L., Nollo, G., Porta, A.: Information domain approach to the investigation of cardio-vascular, cardio-pulmonary, and vasculo-pulmonary causal couplings. Front. Physiol. 2, 80 (2011)CrossRefGoogle Scholar
  14. 14.
    Faes, L., Nollo, G., Porta, A.: Compensated transfer entropy as a tool for reliably estimating information transfer in physiological time series. Entropy 15(1), 198–219 (2013)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Faes, L., Widesott, L., Del Greco, M., Antolini, R., Nollo, G.: Causal cross-spectral analysis of heart rate and blood pressure variability for describing the impairment of the cardiovascular control in neurally mediated syncope. IEEE Trans. Biomed. Eng. 53(1), 65–73 (2006)CrossRefGoogle Scholar
  16. 16.
    Granger, C.W.: Investigating causal relations by econometric models and cross-spectral methods. Econom.: J. Econom. Soc. 37(3), 424–438 (1969)CrossRefGoogle Scholar
  17. 17.
    Granger, C.W.: Testing for causality: a personal view point. J. Econ. Dyn. Control 2, 329–352 (1980)CrossRefGoogle Scholar
  18. 18.
    Javorka, M., Czippelova, B., Turianikova, Z., Lazarova, Z., Tonhajzerova, I., Faes, L.: Causal analysis of short-term cardiovascular variability: state-dependent contribution of feedback and feedforward mechanisms. Med. Biol. Eng. Comput. 55(2), 179–190 (2017)CrossRefGoogle Scholar
  19. 19.
    Li, B.N., Dong, M.C., Vai, M.I.: On an automatic delineator for arterial blood pressure waveforms. Biomed. Signal Process. Control 5(1), 76–81 (2010)CrossRefGoogle Scholar
  20. 20.
    Manikandan, M.S., Soman, K.: A novel method for detecting R-peaks in electrocardiogram (ECG) signal. Biomed. Signal Process. Control 7(2), 118–128 (2012)CrossRefGoogle Scholar
  21. 21.
    Marwaha, P., Sunkaria, R.K.: Exploring total cardiac variability in healthy and pathophysiological subjects using improved refined multiscale entropy. Med. Biol. Eng. Comput. 55(2), 191–205 (2017)CrossRefGoogle Scholar
  22. 22.
    Marwan, N., Zou, Y., Wessel, N., Riedl, M., Kurths, J.: Estimating coupling directions in the cardiorespiratory system using recurrence properties. Philos. Trans. R. Soc. A 371(1997), 20110624 (2013)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Mary, M.H., Singh, D., Deepak, K.: Impact of respiration on cardiovascular coupling using Granger causality analysis in healthy subjects. Biomed. Signal Process. Control 43, 196–203 (2018)CrossRefGoogle Scholar
  24. 24.
    Naidu, V., Reddy, M.: Autoregressive (AR) based power spectral analysis of heart rate time series signal (HRTS signal). In: IEEE Conference on Convergent Technologies for the Asia-Pacific Region TENCON, vol. 4, pp. 1391–1394 (2003)Google Scholar
  25. 25.
    Nollo, G., Faes, L., Porta, A., Antolini, R., Ravelli, F.: Exploring directionality in spontaneous heart period and systolic pressure variability interactions in humans: implications in the evaluation of baroreflex gain. Am. J. Physiol.-Heart Circ. Physiol. 288(4), H1777–H1785 (2005)CrossRefGoogle Scholar
  26. 26.
    Parati, G., Saul, J.P., Di Rienzo, M., Mancia, G.: Spectral analysis of blood pressure and heart rate variability in evaluating cardiovascular regulation: a critical appraisal. Hypertension 25(6), 1276–1286 (1995)CrossRefGoogle Scholar
  27. 27.
    Perlmuter, L.C., Sarda, G., Casavant, V., OHara, K., Hindes, M., Knott, P.T., Mosnaim, A.D.: A review of orthostatic blood pressure regulation and its association with mood and cognition. Clin. Auton. Res. 22(2), 99–107 (2012)CrossRefGoogle Scholar
  28. 28.
    Porta, A., Bassani, T., Bari, V., Tobaldini, E., Takahashi, A.C., Catai, A.M., Montano, N.: Model-based assessment of baroreflex and cardiopulmonary couplings during graded head-up tilt. Comput. Biol. Med. 42(3), 298–305 (2012)CrossRefGoogle Scholar
  29. 29.
    Rangayyan, R.M., Reddy, N.P.: Biomedical signal analysis: a case-study approach. Ann. Biomed. Eng. 30(7), 983–983 (2002)CrossRefGoogle Scholar
  30. 30.
    Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol.-Heart Circ. Physiol. 278(6), H2039–H2049 (2000)CrossRefGoogle Scholar
  31. 31.
    Schulz, S., Adochiei, F.C., Edu, I.R., Schroeder, R., Costin, H., Bar, K.J., Voss, A.: Cardiovascular and cardiorespiratory coupling analyses: a review. Philos. Trans. R. Soc. A 371(1997), 20120191 (2013)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Singh, D., Vinod, K., Saxena, S.C., Deepak, K.K.: Effects of RR segment duration on HRV spectrum estimation. Physiol. Meas. 25(3), 721 (2004)CrossRefGoogle Scholar
  33. 33.
    Steven, M.K.: Modern Spectral Estimation: Theory and Application. Signal Processing Series. American Physiological Society Bethesda, MD (1988)zbMATHGoogle Scholar
  34. 34.
    Stewart, J.M.: Mechanisms of sympathetic regulation in orthostatic intolerance. J. Appl. Physiol. 113(10), 1659–1668 (2012)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Instrumentation and ControlDr. B. R. Ambedkar National Institute of TechnologyJalandharIndia
  2. 2.Department of PhysiologyAll India Institute of Medical ScienceNew DelhiIndia

Personalised recommendations