Data-driven Multiscale Tsallis Complexity: Application to EEG Analysis
Authors: Young-Seok Choi
This work proposes a data-driven multiscale based quantitative measures to reveal the underlying complexity of electroencephalogram (EEG), applying to a rodent model of hypoxic-ischemic brain injury and recovery. Motivated by that real EEG recording is nonlinear and non-stationary over different frequencies or scales, there is a need of more suitable approach over the conventional single scale based tools for analyzing the EEG data. Here, we present a new framework of complexity measures considering changing dynamics over multiple oscillatory scales. The proposed multiscale complexity is obtained by calculating entropies of the probability distributions of the intrinsic mode functions extracted by the empirical mode decomposition (EMD) of EEG. To quantify EEG recording of a rat model of hypoxic-ischemic brain injury following cardiac arrest, the multiscale version of Tsallis entropy is examined. To validate the proposed complexity measure, actual EEG recordings from rats (n=9) experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Experimental results demonstrate that the use of the multiscale Tsallis entropy leads to better discrimination of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective metric as a prognostic tool.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1100783Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1695
 N. V. Thakor and S. Tong, "Advances in quantitative electroencephalogram analysis methods," Annu Rev Biomed Eng, vol. 6, pp. 453-95, 2004.
 O. A. Rosso, "Entropy changes in brain function," Int J Psychophysiol, vol. 64, pp. 75-80, Apr 2007.
 A. Bezerianos, S. Tong, and N. Thakor, "Time-dependent entropy estimation of EEG rhythm changes following brain ischemia," Annals of Biomedical Engineering, vol. 31, pp. 221-232, 2003.
 O. A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schurmann, et al., "Wavelet entropy: a new tool for analysis of short duration brain electrical signals," J Neurosci Methods, vol. 105, pp. 65-75, Jan 30 2001.
 H. C. Shin, S. Tong, S. Yamashita, X. Jia, R. G. Geocadin, and N. V. Thakor, "Quantitative EEG and effect of hypothermia on brain recovery after cardiac arrest," IEEE Trans Biomed Eng, vol. 53, pp. 1016-23, Jun 2006.
 N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," in Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 1998, pp. 903-995.
 C. M. Sweeney-Reed and S. J. Nasuto, "A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition," J Comput Neurosci, vol. 23, pp. 79-111, Aug 2007.
 X. Jia, M. A. Koenig, R. Nickl, G. Zhen, N. V. Thakor, and R. G. Geocadin, "Early electrophysiologic markers predict functional outcome associated with temperature manipulation after cardiac arrest in rats," Crit Care Med, vol. 36, pp. 1909-16, Jun 2008.