Chaos Analysis of Transcranial Doppler Signals for Feature Extraction

Ali Ozturk

Abstract

In this study, chaos theory tools were used for feature extraction from Transcranial Doppler (TCD) signals. The surrogates data sets of the TCD signals which were used for the nonlinearity analysis were extracted as the first feature set. The nonlinear cross prediction errors which were used for the stationary analysis were also extracted for the TCD signals as another feature set. The chaotic invariant features like correlation dimension, maximum Lyapunov exponent, recurrence quantification measures etc. give quantitative values of complexity of the TCD signals. The correlation dimension and maximum Lyapunov exponent were already used as features for classification of TCD signals in the literature. As another chaotic feature set, the statistical quantitative values were extracted from the recurrence plots. The correct calculation of the time delay and the minimum embedding dimension is crucial to correctly estimate all of the chaotic features. These two data were calculated via mutual information and false nearest neighbours approaches, respectively. The space-time separation plots were used in order to find the ideal dimension of Theiler window w which is another important value for the correct estimate of chaotic measures. The reconstructed chaotic attractors with 3-D embedding and 1-step time delay represent the visual phase space portrait of the TCD signals. The attractors were also suggested as another candidate feature set.

References

  1. Casdagli, M., 1997. Recurrence plots revisited. Physica D. 108:12-44.
  2. Eckmann, J.P., Oliffson, K.S., Ruelle, D., 1987. Recurrence plots of dynamical systems. Europhys. Lett. 4(9): 973-977.
  3. Evans, D.H., McDicken, W.N., Skidmore R., Woodcock, J.P., 1989. Doppler Ultrasound: Physics, Instrumentation and Clinical Applications. Wiley, Chichester.
  4. Fraser, A. M., Swinney, H. L. 1986. Independent coordinates for strange attractors from mutual information, Phys. Rev. A 33: 1134.
  5. Guler, I., Hardalac, F., Barisci N., 2002. Application of FFT analyzed cardiac Doppler signals to fuzzy algorithm, Comp. Biol. Med. 32:435-444.
  6. Grassberger, P., Procaccia, I., 1983. Characterization of Strange Attractors. Physical Review Letters, 50:346- 349.
  7. Hegger, R., Kantz, H., Schreiber, T., 1999. Practical implementation of nonlinear time series methods: The TISEAN package. Chaos, 9: 413.
  8. Kennel, M. B. Brown, R., Abarbanel, H. D. I., 1992. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physics Rev. A. 45: 340-353.
  9. Keunen, R.W., Pijlman, H.C., Visee, H.F., Vliegen, J.H., Tavy, D.L., Stam, K.J., 1994. Dynamical chaos determines the variability of transcranial Doppler signals, Neurol. Res. 16: 353-358.
  10. Keunen, R.W., Vliegen, J.H., Stam, C.J., Tavy, D.L., 1996. Nonlinear transcranial Doppler analysis demonstrates age-related changes of cerebral hemodynamics, Ultrasound Med. Biol. 22:383-390.
  11. Kantz, H., 1994. A robust method to estimate the maximal Lyapunov exponent of a time series. Phys. Lett. A. 185: 77-87.
  12. Kantz, H., Schreiber T., 2005. Nonlinear Time Series Analysis, Cambridge University Press.
  13. Ozturk, A., Arslan A., 2007. Classification of transcranial Doppler signals using their chaotic invariant measures, Computer Methods and Programs in Biomedicine, 86(2): 171-180.
  14. Ozturk A., Arslan A., Hardalac F., 2008. Comparison of neuro-fuzzy systems for classification of transcranial Doppler signals with their chaotic invariant measures, Expert Systems with Applications, 34(2): 1044-1055.
  15. Ozturk A., Arslan A., 2015. Neuro-fuzzy Classification of Transcranial Doppler Signals with Chaotic Meaures and Spectral Parameters, 3rd Science and Information Conference, 591-596.
  16. Provenzale, A., Smith, L. A., Vio, R., Murante, G., 1992. Distinguishing between low-dimensional dynamics and randomness in measured time series, Physica D 58, 31.
  17. Schreiber, T., Schmitz, A. 1996. Improved surrogate data for nonlinearity tests, Phys. Rev. Lett. 77, 635.
  18. Schreiber, T., 1997. Detecting and analysing nonstationarity in a time series with nonlinear crosspredictions, Phys. Rev. Lett. 78:843.
  19. Serhatlioglu S., Hardalac F., Guler I., 2003. Classification of transcranial Doppler signals using artificial neural network, J. Med. Syst. 27:205-214.
  20. Sprott, J.C., 2002. Chaos and Time-Series Analysis, Oxford University Pres, New York.
  21. Rosenstein, M. T., Collins, J. J., De Luca, C. J., 1993. A practical method for calculating largest Lyapunov exponents from small data sets, Physica D 65,: 117.
  22. Theiler, J., 1990. Estimating fractal dimension. J. Opt. Soc. Amer. A 7, 1055-1073.
  23. Ubeyli E.D., Guler I., 2005. Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals, Comp. Biol. Med., 35: 687- 702.
  24. Visee, H.F., Keunen, R.W., Pijlman, H.C., Vliegen, J.H., Tavy, D.L., Stam, K.J., Giller, C.A., 1995. The physiological and clinical significance of nonlinear TCD waveform analysis in occlusive cerebrovascular disease, Neurol. Res. 17:384-388.
  25. Vliegen, J.H.R., Stam, C.J., Rombouts, S.A.R., Keunen R.W.M., 1996. Rejection of the 'filtered noise' hypothesis to explain the variability of transcranial Doppler signals: a comparison of original TCD data with Gaussian-scaled phase randomized surrogate data sets, Neurol. Res. 18: 19-24.
  26. Webber, C.L. Jr., Zbilut, J.P., 1994. Dynamical assessment of physiological systems and states using recurrence plot strategies. Journal of Applied Physiology. 76:965-973.
  27. Zbilut, J.P., Guiliani, A., Webber, C.L. Jr., 1998. Recurrence quantification analysis and principle components in the detection of short complex signals. Physics Letters A, 237:131-135.
Download


Paper Citation


in Harvard Style

Ozturk A. (2016). Chaos Analysis of Transcranial Doppler Signals for Feature Extraction . In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 168-174. DOI: 10.5220/0005693701680174


in Bibtex Style

@conference{biosignals16,
author={Ali Ozturk},
title={Chaos Analysis of Transcranial Doppler Signals for Feature Extraction},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)},
year={2016},
pages={168-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005693701680174},
isbn={978-989-758-170-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2016)
TI - Chaos Analysis of Transcranial Doppler Signals for Feature Extraction
SN - 978-989-758-170-0
AU - Ozturk A.
PY - 2016
SP - 168
EP - 174
DO - 10.5220/0005693701680174