Linking Non-Extensive Entropy with Lempel-ziv Complexity to Obtain the Entropic Q-index from EEG Signals

Ernane José Xavier Costa, Adriano Rogeri Bruno Tech, Ana Carolina Sousa Silva

2017

Abstract

Physiological data is generated by process that are either nonlinear deterministic or nondeterministic. The lempel-ziv complexity and non-extensive entropy measurement has been used to quantify information in physiological data like EEG and EMG. When the functions of brain cells are affected by damage caused by several disease it is observed changes in the features of the EEG providing useful insight into brain functions and playing a useful role as a first line of decision-support tool for early detection and diagnosis in brain diseases. This paper uses a method to identify the q-index in those signals by using the relationships between entropy definitions given by Lempel-ziv and those given by Tsallis methods. After all, this article shows that, the q-index can be used to characterize EEG seizure quantifying changes related to the q-entropic index.

References

  1. Al-Nuaimi, A., Jammeh E., H., Sun L., Ifeacho E. 2016. Changes in the EEG amplitude as a biomarker for early detection of Alzheimer's disease. Engineering in Medicine and Biology Society (EMBC) IEEE 38th Annual International Conference, p. 993-996.
  2. Bachmann M., Kalev K., Suhhova A., Lass, J., Hinrikus, H., 2015. Lempel Ziv complexity of EEG in depression. IFMBE Proceedings, v 45, p 58-61.
  3. Bullmore E.; Sporns O., 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature reviews. Neuroscience. 10(3) p.186- 98.
  4. Bullock, T., H., 1989. The micro-EEG represents varied degrees of cooperativity among wide-band generators. In: Basar, E., Bullock, T.H. (Eds.) Brain dynamics, Progress and perspectives, Berlim, Springer-Verlag, p.5-12.
  5. Gómez C., Hornero R,, Abásolo D., Fernández A., López M., 2006. Complexity analysis of the magnetoencephalogram background activity in Alzeimer's disease patients. Medical Engineering and Physics. v.28, n.9, p.851-859.
  6. Lempel, A.; Ziv, J., 1976. On the complexity of finite sequences. IEEE Transactions, IT-22, p.75-81.
  7. McBride, J., Zhao, X., Nichols, T., Vagnini, V., Munro, N., Berry, D., Jiang, Y., 2013. Scalp EEG-Based Discrimination of Cognitive Deficits After Traumatic Brain Injury Using Event-Related Tsallis Entropy Analysis. IEEE Transactions on Biomedical Engineering, v 60, n 1, p 90-96.
  8. Nagarajan, R., 2002. Quantifying physiological data with Lempel-Ziv complexity - certain issues. Transactions on Biomedical Engneering, v.49, n.11, p.1371-73, 2002.
  9. Nagarajan, R., Szczepanski, J., Wajnryb, E., 2008. Interpreting non-random signatures in biomedical signal with Lempel-Ziv complexity. Physica D, v.237, p.359-64.
  10. Pei, X.. Zheng C., He W., Xu J,. 2006. Quantitative measure of complexity of the dynamic event-related EEG data. Neurocomputing, v.70, p.263-72.
  11. Rajkovic M., 2004. Entropic nonextensivity as a measure of time series complexity. Physica A, v.341 p.327-333.
  12. Richman J., Moorman J. R., 2000. Physiological time series analysis using approximate entropy and sample entropy. The American Journal of Physiology Heart and Circulatory Physiology, v. 278, p.2039-2049.
  13. Sabeti, M.; Katebi, S. Boostani R., 2009. Entropy and complexity measures for EEG signal classification of schizophenic and control participants. Artificial intelligence in medicine, 47(3):263-74.
  14. Szczpánski, J., Amigó J. M., Wajnry E., Sanchez-Vives M. V., 2003. Application of Lempel-Ziv complexity to the analysis of neural discharges. Network: Computing in neural systems, v.14, p.335-50.
  15. Tong S., Bezerianosa A., Paula, J., Zhu, Y., Thakor, N. 2002. Nonextensive entropy measure of EEG following brain injury from cardiac arrest. Physica A, v.305, p.619-628.
  16. Tsallis, C. 1988. Possible generalization of BoltzmanGibbs statistics. Journal of statistical physics, v.52, n.1/2, p.479-87.
  17. Tsallis, C.; Plastino, A., R., Zheng, w. (1997). Power-law sensitivity to initial conditions - new entropic representation. Chaos, Solitons and fractals. v.8, n.6, p885-91.
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Paper Citation


in Harvard Style

Costa E., Tech A. and Sousa Silva A. (2017). Linking Non-Extensive Entropy with Lempel-ziv Complexity to Obtain the Entropic Q-index from EEG Signals . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 101-105. DOI: 10.5220/0006077901010105


in Bibtex Style

@conference{biosignals17,
author={Ernane José Xavier Costa and Adriano Rogeri Bruno Tech and Ana Carolina Sousa Silva},
title={Linking Non-Extensive Entropy with Lempel-ziv Complexity to Obtain the Entropic Q-index from EEG Signals},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={101-105},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006077901010105},
isbn={978-989-758-212-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - Linking Non-Extensive Entropy with Lempel-ziv Complexity to Obtain the Entropic Q-index from EEG Signals
SN - 978-989-758-212-7
AU - Costa E.
AU - Tech A.
AU - Sousa Silva A.
PY - 2017
SP - 101
EP - 105
DO - 10.5220/0006077901010105